R Notebook
library(Seurat)The legacy packages maptools, rgdal, and rgeos, underpinning this package
will retire shortly. Please refer to R-spatial evolution reports on
https://r-spatial.org/r/2023/05/15/evolution4.html for details.
This package is now running under evolution status 0
Registered S3 method overwritten by 'data.table':
method from
print.data.table
Registered S3 method overwritten by 'htmlwidgets':
method from
print.htmlwidget tools:rstudio
Attaching SeuratObject
Warning message:
R graphics engine version 15 is not supported by this version of RStudio. The Plots tab will be disabled until a newer version of RStudio is installed. library(tidyverse)Registered S3 methods overwritten by 'dbplyr':
method from
print.tbl_lazy
print.tbl_sql ── Attaching packages ───────────────────────── tidyverse 1.3.2 ──
✔ ggplot2 3.4.2 ✔ purrr 1.0.1
✔ tibble 3.2.1 ✔ dplyr 1.1.2
✔ tidyr 1.3.0 ✔ stringr 1.5.0
✔ readr 2.1.3 ✔ forcats 0.5.2
── Conflicts ──────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag() masks stats::lag()#library(CelltypeR)samples
[1] "NPC3383" "NPC3383-iso" "NPC3575" "NPC3575-iso"
[5] "NPC3595" "NPC3595-iso" "NPC3940" "NPC3940N-iso"
[9] "NPC2965" "NPC3567" "NPC3567-iso" "NPCTD06"
[13] "NPCTD07" "NPC3448" "NPCTD06-iso" "NPC3123"
[17] "NPCQ65Q" "NPCAIW002-02" "NPC3940R-iso" "Final3123"
[21] "FinalP65P" "FinalQ65Q" "NPCP65P" "Final3383-iso"
[25] "Final3575-iso" "Final3595" "Final3595-iso" "Final3940"
[29] "Final3940N-iso" "Final3567" "NPCAIW001-02" "NPC2AIW002-02"
[33] "NPCTD22" "FinalAIW001-02" "FinalAIW002-02" "FinalTD07"
[37] "M1" "M2" "M3" "M4"
[41] "M5" "M6" "M7" "M8"
[45] "POOL_1NPC" "POOL_2NPC" "POOL_3NPC" "POOL_4Final" Add meta data information
DimPlot(seu)
# check with sample names
table(seu$sample, seu$CultureType)
NPC Neurons2weeks MotorNeurons
Final3123 0 796 0
Final3383-iso 0 1008 0
Final3567 0 940 0
Final3575-iso 0 1221 0
Final3595 0 1302 0
Final3595-iso 0 1546 0
Final3940 0 1643 0
Final3940N-iso 0 1238 0
FinalAIW001-02 0 1712 0
FinalAIW002-02 0 1439 0
FinalP65P 0 1709 0
FinalQ65Q 0 2739 0
FinalTD07 0 1032 0
M1 0 0 1290
M2 0 0 1627
M3 0 0 2463
M4 0 0 1883
M5 0 0 8202
M6 0 0 1160
M7 0 0 1968
M8 0 0 1215
NPC2965 279 0 0
NPC2AIW002-02 1478 0 0
NPC3123 512 0 0
NPC3383 800 0 0
NPC3383-iso 1030 0 0
NPC3448 1922 0 0
NPC3567 920 0 0
NPC3567-iso 1372 0 0
NPC3575 155 0 0
NPC3575-iso 1998 0 0
NPC3595 798 0 0
NPC3595-iso 1300 0 0
NPC3940 414 0 0
NPC3940N-iso 846 0 0
NPC3940R-iso 1105 0 0
NPCAIW001-02 1252 0 0
NPCAIW002-02 1840 0 0
NPCP65P 1146 0 0
NPCQ65Q 862 0 0
NPCTD06 1432 0 0
NPCTD06-iso 591 0 0
NPCTD07 1083 0 0
NPCTD22 1507 0 0
POOL_1NPC 876 0 0
POOL_2NPC 204 0 0
POOL_3NPC 1218 0 0
POOL_4Final 0 977 0Add the lines and then the diseases status
seu <- annotate(seu, annotations = new_line_vector, to_label = "sample",annotation_name = "Line")
DimPlot(seu)
# check
table(seu$sample, seu$Line)
3383 3383-iso 3575 3575-iso 3595 3595-iso 3940 3940N-iso
Final3123 0 0 0 0 0 0 0 0
Final3383-iso 0 1008 0 0 0 0 0 0
Final3567 0 0 0 0 0 0 0 0
Final3575-iso 0 0 0 1221 0 0 0 0
Final3595 0 0 0 0 1302 0 0 0
Final3595-iso 0 0 0 0 0 1546 0 0
Final3940 0 0 0 0 0 0 1643 0
Final3940N-iso 0 0 0 0 0 0 0 1238
FinalAIW001-02 0 0 0 0 0 0 0 0
FinalAIW002-02 0 0 0 0 0 0 0 0
FinalP65P 0 0 0 0 0 0 0 0
FinalQ65Q 0 0 0 0 0 0 0 0
FinalTD07 0 0 0 0 0 0 0 0
M1 0 0 0 0 0 0 0 0
M2 0 0 0 0 0 0 0 0
M3 0 0 0 0 0 0 0 0
M4 0 0 0 0 0 0 0 0
M5 0 0 0 0 0 0 0 0
M6 0 0 0 0 0 0 0 0
M7 0 0 0 0 0 0 0 0
M8 0 0 0 0 0 0 0 0
NPC2965 0 0 0 0 0 0 0 0
NPC2AIW002-02 0 0 0 0 0 0 0 0
NPC3123 0 0 0 0 0 0 0 0
NPC3383 800 0 0 0 0 0 0 0
NPC3383-iso 0 1030 0 0 0 0 0 0
NPC3448 0 0 0 0 0 0 0 0
NPC3567 0 0 0 0 0 0 0 0
NPC3567-iso 0 0 0 0 0 0 0 0
2965 3567 3567-iso TD06 TD07 3448 TD06-iso 3123-iso
Final3123 0 0 0 0 0 0 0 796
Final3383-iso 0 0 0 0 0 0 0 0
Final3567 0 940 0 0 0 0 0 0
Final3575-iso 0 0 0 0 0 0 0 0
Final3595 0 0 0 0 0 0 0 0
Final3595-iso 0 0 0 0 0 0 0 0
Final3940 0 0 0 0 0 0 0 0
Final3940N-iso 0 0 0 0 0 0 0 0
FinalAIW001-02 0 0 0 0 0 0 0 0
FinalAIW002-02 0 0 0 0 0 0 0 0
FinalP65P 0 0 0 0 0 0 0 0
FinalQ65Q 0 0 0 0 0 0 0 0
FinalTD07 0 0 0 0 1032 0 0 0
M1 0 0 0 0 0 0 0 0
M2 0 0 0 0 0 0 0 0
M3 0 0 0 0 0 0 0 0
M4 0 0 0 0 0 0 0 0
M5 0 0 0 0 0 0 0 0
M6 0 0 0 0 0 0 0 0
M7 0 0 0 0 0 0 0 0
M8 0 0 0 0 0 0 0 0
NPC2965 279 0 0 0 0 0 0 0
NPC2AIW002-02 0 0 0 0 0 0 0 0
NPC3123 0 0 0 0 0 0 0 512
NPC3383 0 0 0 0 0 0 0 0
NPC3383-iso 0 0 0 0 0 0 0 0
NPC3448 0 0 0 0 0 1922 0 0
NPC3567 0 920 0 0 0 0 0 0
NPC3567-iso 0 0 1372 0 0 0 0 0
3123-isoQ65Q AIW002-02 3940R-iso 3123-isoP65P AIW001-02
Final3123 0 0 0 0 0
Final3383-iso 0 0 0 0 0
Final3567 0 0 0 0 0
Final3575-iso 0 0 0 0 0
Final3595 0 0 0 0 0
Final3595-iso 0 0 0 0 0
Final3940 0 0 0 0 0
Final3940N-iso 0 0 0 0 0
FinalAIW001-02 0 0 0 0 1712
FinalAIW002-02 0 1439 0 0 0
FinalP65P 0 0 0 1709 0
FinalQ65Q 2739 0 0 0 0
FinalTD07 0 0 0 0 0
M1 0 0 0 0 0
M2 0 0 0 0 0
M3 0 0 0 0 0
M4 0 0 0 0 0
M5 0 0 0 0 0
M6 0 0 0 0 0
M7 0 0 0 0 0
M8 0 0 0 0 0
NPC2965 0 0 0 0 0
NPC2AIW002-02 0 1478 0 0 0
NPC3123 0 0 0 0 0
NPC3383 0 0 0 0 0
NPC3383-iso 0 0 0 0 0
NPC3448 0 0 0 0 0
NPC3567 0 0 0 0 0
NPC3567-iso 0 0 0 0 0
TD22 M1 M2 M3 M4 M5 M6 M7 M8 POOL_1NPC
Final3123 0 0 0 0 0 0 0 0 0 0
Final3383-iso 0 0 0 0 0 0 0 0 0 0
Final3567 0 0 0 0 0 0 0 0 0 0
Final3575-iso 0 0 0 0 0 0 0 0 0 0
Final3595 0 0 0 0 0 0 0 0 0 0
Final3595-iso 0 0 0 0 0 0 0 0 0 0
Final3940 0 0 0 0 0 0 0 0 0 0
Final3940N-iso 0 0 0 0 0 0 0 0 0 0
FinalAIW001-02 0 0 0 0 0 0 0 0 0 0
FinalAIW002-02 0 0 0 0 0 0 0 0 0 0
FinalP65P 0 0 0 0 0 0 0 0 0 0
FinalQ65Q 0 0 0 0 0 0 0 0 0 0
FinalTD07 0 0 0 0 0 0 0 0 0 0
M1 0 1290 0 0 0 0 0 0 0 0
M2 0 0 1627 0 0 0 0 0 0 0
M3 0 0 0 2463 0 0 0 0 0 0
M4 0 0 0 0 1883 0 0 0 0 0
M5 0 0 0 0 0 8202 0 0 0 0
M6 0 0 0 0 0 0 1160 0 0 0
M7 0 0 0 0 0 0 0 1968 0 0
M8 0 0 0 0 0 0 0 0 1215 0
NPC2965 0 0 0 0 0 0 0 0 0 0
NPC2AIW002-02 0 0 0 0 0 0 0 0 0 0
NPC3123 0 0 0 0 0 0 0 0 0 0
NPC3383 0 0 0 0 0 0 0 0 0 0
NPC3383-iso 0 0 0 0 0 0 0 0 0 0
NPC3448 0 0 0 0 0 0 0 0 0 0
NPC3567 0 0 0 0 0 0 0 0 0 0
NPC3567-iso 0 0 0 0 0 0 0 0 0 0
POOL_2NPC POOL_3NPC POOL_4Final
Final3123 0 0 0
Final3383-iso 0 0 0
Final3567 0 0 0
Final3575-iso 0 0 0
Final3595 0 0 0
Final3595-iso 0 0 0
Final3940 0 0 0
Final3940N-iso 0 0 0
FinalAIW001-02 0 0 0
FinalAIW002-02 0 0 0
FinalP65P 0 0 0
FinalQ65Q 0 0 0
FinalTD07 0 0 0
M1 0 0 0
M2 0 0 0
M3 0 0 0
M4 0 0 0
M5 0 0 0
M6 0 0 0
M7 0 0 0
M8 0 0 0
NPC2965 0 0 0
NPC2AIW002-02 0 0 0
NPC3123 0 0 0
NPC3383 0 0 0
NPC3383-iso 0 0 0
NPC3448 0 0 0
NPC3567 0 0 0
NPC3567-iso 0 0 0
[ reached getOption("max.print") -- omitted 19 rows ]DimPlot(seu)
# add disease status
# Create the lookup table for Line to DiseaseStatus mapping
line_to_disease <- c("3448" = "HC",
"TD22" = "HC",
"AIW001-02" = "HC",
"AIW002-02" = "HC",
"2965" = "PD",
"3383" = "PD",
"3575" = "PD",
"TD06" = "PD",
"TD07" = "PD",
"3123" = "PD",
"3567" = "PD",
"3595" = "PD",
"3940" = "PD",
"3567-iso" = "PD-iso",
"3940R-iso" = "PD-iso",
"TD06-iso" = "PD-iso",
"3940N-iso" = "PD-iso",
"3123-isoQ65Q" = "PD-iso",
"3123-isoP65P" = "PD-iso",
"3123-iso" = "PD-iso",
"3383-iso" = "PD-iso",
"3575-iso" = "PD-iso",
"3595-iso" = "PD-iso")
# Assuming "Line" is your starting character vector
Idents(seu) <- "Line"
Line <- levels(seu)
# Create a new vector of disease statuses using the lookup table
disease_status_vector <- ifelse(Line %in% names(line_to_disease),
line_to_disease[Line],
"other")
# Print the new vector of disease statuses
print(disease_status_vector) [1] "PD" "PD-iso" "PD" "PD-iso" "PD" "PD-iso" "PD"
[8] "PD-iso" "PD" "PD" "PD-iso" "PD" "PD" "HC"
[15] "PD-iso" "PD-iso" "PD-iso" "HC" "PD-iso" "PD-iso" "HC"
[22] "HC" "other" "other" "other" "other" "other" "other"
[29] "other" "other" "other" "other" "other" "other" seu <- annotate(seu, annotations = disease_status_vector, to_label = "Line",annotation_name = "DiseaseStatus")
# check
table(seu$Line, seu$DiseaseStatus)
PD PD-iso HC other
3383 800 0 0 0
3383-iso 0 2038 0 0
3575 155 0 0 0
3575-iso 0 3219 0 0
3595 2100 0 0 0
3595-iso 0 2846 0 0
3940 2057 0 0 0
3940N-iso 0 2084 0 0
2965 279 0 0 0
3567 1860 0 0 0
3567-iso 0 1372 0 0
TD06 1432 0 0 0
TD07 2115 0 0 0
3448 0 0 1922 0
TD06-iso 0 591 0 0
3123-iso 0 1308 0 0
3123-isoQ65Q 0 3601 0 0
AIW002-02 0 0 4757 0
3940R-iso 0 1105 0 0
3123-isoP65P 0 2855 0 0
AIW001-02 0 0 2964 0
TD22 0 0 1507 0
M1 0 0 0 1290
M2 0 0 0 1627
M3 0 0 0 2463
M4 0 0 0 1883
M5 0 0 0 8202
M6 0 0 0 1160
M7 0 0 0 1968
M8 0 0 0 1215
POOL_1NPC 0 0 0 876
POOL_2NPC 0 0 0 204
POOL_3NPC 0 0 0 1218
POOL_4Final 0 0 0 977table(sample)Error in unique.default(x, nmax = nmax) :
unique() applies only to vectorscellcounts <- as.data.frame(table(seu$Line,seu$CultureType))
table(seu$Line,seu$CultureType,seu$DiseaseStatus), , = PD
NPC Neurons2weeks MotorNeurons
3383 800 0 0
3383-iso 0 0 0
3575 155 0 0
3575-iso 0 0 0
3595 798 1302 0
3595-iso 0 0 0
3940 414 1643 0
3940N-iso 0 0 0
2965 279 0 0
3567 920 940 0
3567-iso 0 0 0
TD06 1432 0 0
TD07 1083 1032 0
3448 0 0 0
TD06-iso 0 0 0
3123-iso 0 0 0
3123-isoQ65Q 0 0 0
AIW002-02 0 0 0
3940R-iso 0 0 0
3123-isoP65P 0 0 0
AIW001-02 0 0 0
TD22 0 0 0
M1 0 0 0
M2 0 0 0
M3 0 0 0
M4 0 0 0
M5 0 0 0
M6 0 0 0
M7 0 0 0
M8 0 0 0
POOL_1NPC 0 0 0
POOL_2NPC 0 0 0
POOL_3NPC 0 0 0
POOL_4Final 0 0 0
, , = PD-iso
NPC Neurons2weeks MotorNeurons
3383 0 0 0
3383-iso 1030 1008 0
3575 0 0 0
3575-iso 1998 1221 0
3595 0 0 0
3595-iso 1300 1546 0
3940 0 0 0
3940N-iso 846 1238 0
2965 0 0 0
3567 0 0 0
3567-iso 1372 0 0
TD06 0 0 0
TD07 0 0 0
3448 0 0 0
TD06-iso 591 0 0
3123-iso 512 796 0
3123-isoQ65Q 862 2739 0
AIW002-02 0 0 0
3940R-iso 1105 0 0
3123-isoP65P 1146 1709 0
AIW001-02 0 0 0
TD22 0 0 0
M1 0 0 0
M2 0 0 0
M3 0 0 0
M4 0 0 0
M5 0 0 0
M6 0 0 0
M7 0 0 0
M8 0 0 0
POOL_1NPC 0 0 0
POOL_2NPC 0 0 0
POOL_3NPC 0 0 0
POOL_4Final 0 0 0
, , = HC
NPC Neurons2weeks MotorNeurons
3383 0 0 0
3383-iso 0 0 0
3575 0 0 0
3575-iso 0 0 0
3595 0 0 0
3595-iso 0 0 0
3940 0 0 0
3940N-iso 0 0 0
2965 0 0 0
3567 0 0 0
3567-iso 0 0 0
TD06 0 0 0
TD07 0 0 0
3448 1922 0 0
TD06-iso 0 0 0
3123-iso 0 0 0
3123-isoQ65Q 0 0 0
AIW002-02 3318 1439 0
3940R-iso 0 0 0
3123-isoP65P 0 0 0
AIW001-02 1252 1712 0
TD22 1507 0 0
M1 0 0 0
M2 0 0 0
M3 0 0 0
M4 0 0 0
M5 0 0 0
M6 0 0 0
M7 0 0 0
M8 0 0 0
POOL_1NPC 0 0 0
POOL_2NPC 0 0 0
POOL_3NPC 0 0 0
POOL_4Final 0 0 0
, , = other
NPC Neurons2weeks MotorNeurons
3383 0 0 0
3383-iso 0 0 0
3575 0 0 0
3575-iso 0 0 0
3595 0 0 0
3595-iso 0 0 0
3940 0 0 0
3940N-iso 0 0 0
2965 0 0 0
3567 0 0 0
3567-iso 0 0 0
TD06 0 0 0
TD07 0 0 0
3448 0 0 0
TD06-iso 0 0 0
3123-iso 0 0 0
3123-isoQ65Q 0 0 0
AIW002-02 0 0 0
3940R-iso 0 0 0
3123-isoP65P 0 0 0
AIW001-02 0 0 0
TD22 0 0 0
M1 0 0 1290
M2 0 0 1627
M3 0 0 2463
M4 0 0 1883
M5 0 0 8202
M6 0 0 1160
M7 0 0 1968
M8 0 0 1215
POOL_1NPC 876 0 0
POOL_2NPC 204 0 0
POOL_3NPC 1218 0 0
POOL_4Final 0 977 0# Convert long_df to the wide format
wide_df <- pivot_wider(cellcounts,
id_cols = Var1,
names_from = Var2,
values_from = Freq)
write.csv(wide_df,"cellcounts_sample.csv")
DimPlot(seu, group.by = "DiseaseStatus")
Subset for to remove pools and Maria’s samples
Reprocess subset
seu.n <- readRDS("/Users/rhalenathomas/Documents/Data/scRNAseq/ParseExample/Ex2_48well/ParseNCADDsamples.RDS")
Get clusters
Subset out neurons and NPC
#seu.n$CultureType
Idents(seu.n) <- "CultureType"
neurons <- subset(seu.n, idents = "Neurons2weeks")
DimPlot(seu)
DimPlot(seu, group.by = "CultureType")
DimPlot(seu, group.by = "Line")
save objects
neurons <- readRDS("/Users/rhalenathomas/Documents/Data/scRNAseq/ParseExample/Ex2_48well/ParseNCADDsamplesNeurons.RDS")
*** recursive gc invocation
*** recursive gc invocation
*** recursive gc invocation
*** recursive gc invocation
*** recursive gc invocation
*** recursive gc invocation
*** recursive gc invocation
*** recursive gc invocation
*** recursive gc invocation
*** recursive gc invocation
*** recursive gc invocation
The legacy packages maptools, rgdal, and rgeos, underpinning this package
will retire shortly. Please refer to R-spatial evolution reports on
https://r-spatial.org/r/2023/05/15/evolution4.html for details.
This package is now running under evolution status 0
Registered S3 method overwritten by 'data.table':
method from
print.data.table
Registered S3 method overwritten by 'htmlwidgets':
method from
print.htmlwidget tools:rstudio
Warning message:
R graphics engine version 15 is not supported by this version of RStudio. The Plots tab will be disabled until a newer version of RStudio is installed. table(neurons$Line)
3383 3383-iso 3575 3575-iso 3595
0 1008 0 1221 1302
3595-iso 3940 3940N-iso 2965 3567
1546 1643 1238 0 940
3567-iso TD06 TD07 3448 TD06-iso
0 0 1032 0 0
3123-iso 3123-isoQ65Q AIW002-02 3940R-iso 3123-isoP65P
796 2739 1439 0 1709
AIW001-02 TD22 M1 M2 M3
1712 0 0 0 0
M4 M5 M6 M7 M8
0 0 0 0 0
POOL_1NPC POOL_2NPC POOL_3NPC POOL_4Final
0 0 0 0 Idents(neurons) <- "Line"
levels(neurons) [1] "3383-iso" "3575-iso" "3595" "3595-iso"
[5] "3940" "3940N-iso" "3567" "TD07"
[9] "3123-iso" "3123-isoQ65Q" "AIW002-02" "3123-isoP65P"
[13] "AIW001-02" table(neurons$DiseaseStatus,neurons$Line)
3383 3383-iso 3575 3575-iso 3595 3595-iso 3940 3940N-iso
PD 0 0 0 0 1302 0 1643 0
PD-iso 0 1008 0 1221 0 1546 0 1238
HC 0 0 0 0 0 0 0 0
other 0 0 0 0 0 0 0 0
2965 3567 3567-iso TD06 TD07 3448 TD06-iso 3123-iso
PD 0 940 0 0 1032 0 0 0
PD-iso 0 0 0 0 0 0 0 796
HC 0 0 0 0 0 0 0 0
other 0 0 0 0 0 0 0 0
3123-isoQ65Q AIW002-02 3940R-iso 3123-isoP65P AIW001-02
PD 0 0 0 0 0
PD-iso 2739 0 0 1709 0
HC 0 1439 0 0 1712
other 0 0 0 0 0
TD22 M1 M2 M3 M4 M5 M6 M7 M8 POOL_1NPC
PD 0 0 0 0 0 0 0 0 0 0
PD-iso 0 0 0 0 0 0 0 0 0 0
HC 0 0 0 0 0 0 0 0 0 0
other 0 0 0 0 0 0 0 0 0 0
POOL_2NPC POOL_3NPC POOL_4Final
PD 0 0 0
PD-iso 0 0 0
HC 0 0 0
other 0 0 0Now integrate samples
# All samples is too large to integrate
Idents(neurons) <- "DiseaseStatus"
levels(neurons)[1] "PD" "PD-iso" "HC" neur.PD <- subset(neurons, idents = "PD")
neur.PDiso <- subset(neurons, idents = "PD-iso")
neur.HC <- subset(neurons, idents = "HC")
# make a list of seurat objects by our cell type variable
# will integrate the PD, PD-iso, HC separately and then merge them will see if will integrate or not
# an integrate function
# Define a function for integrating a list of Seurat objects
integrate_seurat_objects <- function(seurat_list, dims = 1:30) {
# Normalize and find variable features for each object
for (i in 1:length(seurat_list)) {
seurat_list[[i]] <- NormalizeData(seurat_list[[i]], verbose = FALSE)
seurat_list[[i]] <- FindVariableFeatures(seurat_list[[i]], selection.method = "vst")
}
# Create an empty Seurat object to store the integrated data
integrated_seurat <- subset(seurat_list[[1]])
# Iterate over the list of Seurat objects
for (i in 1:length(seurat_list)) {
# Rename the 'orig.ident' metadata inside the Seurat object to match the object name in the list
seurat_list[[i]]$orig.ident <- names(seurat_list)[i]
}
sample.list <- seurat_list
for (i in 1:length(sample.list)) {
# Normalize and scale the data
sample.list[[i]] <- NormalizeData(sample.list[[i]], verbose = FALSE)
sample.list[[i]] <- ScaleData(sample.list[[i]], verbose = FALSE)
# Find variable features
sample.list[[i]] <- FindVariableFeatures(sample.list[[i]], selection.method = "vst")
# Get the variable features
variable_features <- VariableFeatures(sample.list[[i]])
# Run PCA with the variable features
sample.list[[i]] <- RunPCA(sample.list[[i]], verbose = FALSE, npcs = 30, features = variable_features)
}
# Find integration anchors
int.anchors <- FindIntegrationAnchors(object.list = sample.list, dims = dims, reduction = "rpca")
# Integrate data
integrated_seurat <- IntegrateData(anchorset = int.anchors, dims = dims)
return(integrated_seurat)
}
Use function to integrate the HC
sublist <- SplitObject(neur.PDiso, split.by = "Line")
int.PDiso <- integrate_seurat_objects(sublist, dims = 1:30)
Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Computing 2000 integration features
Scaling features for provided objects
| | 0 % ~calculating
|++++++++ | 14% ~01s
|+++++++++++++++ | 29% ~01s
|++++++++++++++++++++++ | 43% ~01s
|+++++++++++++++++++++++++++++ | 57% ~01s
|++++++++++++++++++++++++++++++++++++ | 71% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=02s Computing within dataset neighborhoods
| | 0 % ~calculating
|++++++++ | 14% ~01s
|+++++++++++++++ | 29% ~02s
|++++++++++++++++++++++ | 43% ~02s
|+++++++++++++++++++++++++++++ | 57% ~01s
|++++++++++++++++++++++++++++++++++++ | 71% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=03s Finding all pairwise anchors
| | 0 % ~calculating Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 1404 anchors
|+++ | 5 % ~44s Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 1428 anchors
|+++++ | 10% ~58s Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 2252 anchors
|++++++++ | 14% ~01m 00s Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 502 anchors
|++++++++++ | 19% ~51s Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 664 anchors
|++++++++++++ | 24% ~46s Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 630 anchors
|+++++++++++++++ | 29% ~44s Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 925 anchors
|+++++++++++++++++ | 33% ~40s Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 1152 anchors
|++++++++++++++++++++ | 38% ~37s Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 1390 anchors
|++++++++++++++++++++++ | 43% ~36s Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 602 anchors
|++++++++++++++++++++++++ | 48% ~32s Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 525 anchors
|+++++++++++++++++++++++++++ | 52% ~29s Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 653 anchors
|+++++++++++++++++++++++++++++ | 57% ~27s Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 709 anchors
|+++++++++++++++++++++++++++++++ | 62% ~25s Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 336 anchors
|++++++++++++++++++++++++++++++++++ | 67% ~21s Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 665 anchors
|++++++++++++++++++++++++++++++++++++ | 71% ~18s Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 467 anchors
|+++++++++++++++++++++++++++++++++++++++ | 76% ~15s Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 613 anchors
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~12s Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 777 anchors
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~09s Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 279 anchors
|++++++++++++++++++++++++++++++++++++++++++++++ | 90% ~06s Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 652 anchors
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~03s Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 856 anchors
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=59s Merging dataset 1 into 3
Extracting anchors for merged samples
Finding integration vectors
Finding integration vector weights
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Integrating data
Merging dataset 2 into 3 1
Extracting anchors for merged samples
Finding integration vectors
Finding integration vector weights
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Integrating data
Merging dataset 5 into 3 1 2
Extracting anchors for merged samples
Finding integration vectors
Finding integration vector weights
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Integrating data
Merging dataset 7 into 6
Extracting anchors for merged samples
Finding integration vectors
Finding integration vector weights
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Integrating data
Merging dataset 4 into 3 1 2 5
Extracting anchors for merged samples
Finding integration vectors
Finding integration vector weights
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Integrating data
Merging dataset 6 7 into 3 1 2 5 4
Extracting anchors for merged samples
Finding integration vectors
Finding integration vector weights
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Integrating dataintegrated_seurat <- integrate_seurat_objects(Neuron_list, dims = 1:20)
Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Computing 2000 integration features
Scaling features for provided objects
| | 0 % ~calculating
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=01s Computing within dataset neighborhoods
| | 0 % ~calculating
|+++++++++++++++++++++++++ | 50% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=02s Finding all pairwise anchors
| | 0 % ~calculating Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 905 anchors
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=05s Merging dataset 1 into 2
Extracting anchors for merged samples
Finding integration vectors
Finding integration vector weights
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Integrating data# save integrated objects
saveRDS(int.HC, "/Users/rhalenathomas/Documents/Data/scRNAseq/ParseExample/Ex2_48well/ParseNCADDsamplesNeuronsintHC.RDS")
saveRDS(int.PD, "/Users/rhalenathomas/Documents/Data/scRNAseq/ParseExample/Ex2_48well/ParseNCADDsamplesNeuronsintPD.RDS")
saveRDS(int.PDiso, "/Users/rhalenathomas/Documents/Data/scRNAseq/ParseExample/Ex2_48well/ParseNCADDsamplesNeuronsintPDiso.RDS")
Read in the integrated objects and then integrate all 3
int.HC <- readRDS("/Users/rhalenathomas/Documents/Data/scRNAseq/ParseExample/Ex2_48well/ParseNCADDsamplesNeuronsintHC.RDS")
int.PD <- readRDS("/Users/rhalenathomas/Documents/Data/scRNAseq/ParseExample/Ex2_48well/ParseNCADDsamplesNeuronsintPD.RDS")Loading required package: SeuratObject
Loading required package: sp
The legacy packages maptools, rgdal, and rgeos, underpinning this package
will retire shortly. Please refer to R-spatial evolution reports on
https://r-spatial.org/r/2023/05/15/evolution4.html for details.
This package is now running under evolution status 0 int.PDiso("/Users/rhalenathomas/Documents/Data/scRNAseq/ParseExample/Ex2_48well/ParseNCADDsamplesNeuronsintPD.RDS")Error in int.PDiso("/Users/rhalenathomas/Documents/Data/scRNAseq/ParseExample/Ex2_48well/ParseNCADDsamplesNeuronsintPD.RDS") :
could not find function "int.PDiso"Integrated only the PD and HC
seu <- RunPCA(seu, npcs = 20, verbose = FALSE)
seu <- RunUMAP(seu, reduction = "pca", dims = 1:20, n.neighbors = 81)
Warning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session
22:08:22 UMAP embedding parameters a = 0.9922 b = 1.112
22:08:22 Read 8068 rows and found 20 numeric columns
22:08:22 Using Annoy for neighbor search, n_neighbors = 81
22:08:22 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:08:23 Writing NN index file to temp file /var/folders/k4/khtkczkd5tn732ftjpwgtr240000gn/T//Rtmp2Z2lqE/filede53781a1af9
22:08:23 Searching Annoy index using 1 thread, search_k = 8100
22:08:29 Annoy recall = 100%
22:08:29 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 81
22:08:31 Initializing from normalized Laplacian + noise (using irlba)
22:08:31 Commencing optimization for 500 epochs, with 817690 positive edges
Using method 'umap'
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:08:59 Optimization finishedseu <- FindVariableFeatures(seu)Warning in FindVariableFeatures.Assay(object = assay.data, selection.method = selection.method, :
selection.method set to 'vst' but count slot is empty; will use data slot instead
Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Warning in eval(predvars, data, env) : NaNs produced
Warning in hvf.info$variance.expected[not.const] <- 10^fit$fitted :
number of items to replace is not a multiple of replacement length
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|seu <- FindNeighbors(seu, dims = 1:20, k.param = 81)Computing nearest neighbor graph
Computing SNNseu <- FindClusters(seu, resolution = 0.3)Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 8068
Number of edges: 948317
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.9346
Number of communities: 12
Elapsed time: 2 secondsDimPlot(seu)
DimPlot(seu, group.by = "Line")
DimPlot(seu, group.by = "DiseaseStatus")
Samples are not well integrated. I’ll try to integrate by lines
int.PDHC <- integrate_seurat_objects(sublist, dims = 1:20)
Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Computing 2000 integration features
Scaling features for provided objects
| | 0 % ~calculating
|+++++++++ | 17% ~01s
|+++++++++++++++++ | 33% ~01s
|+++++++++++++++++++++++++ | 50% ~01s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=01s Computing within dataset neighborhoods
| | 0 % ~calculating
|+++++++++ | 17% ~02s
|+++++++++++++++++ | 33% ~01s
|+++++++++++++++++++++++++ | 50% ~01s
|++++++++++++++++++++++++++++++++++ | 67% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=02s Finding all pairwise anchors
| | 0 % ~calculating Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 465 anchors
|++++ | 7 % ~31s Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 328 anchors
|+++++++ | 13% ~26s Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 291 anchors
|++++++++++ | 20% ~24s Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 214 anchors
|++++++++++++++ | 27% ~23s Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 264 anchors
|+++++++++++++++++ | 33% ~21s Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 202 anchors
|++++++++++++++++++++ | 40% ~19s Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 194 anchors
|++++++++++++++++++++++++ | 47% ~17s Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 433 anchors
|+++++++++++++++++++++++++++ | 53% ~15s Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 472 anchors
|++++++++++++++++++++++++++++++ | 60% ~12s Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 217 anchors
|++++++++++++++++++++++++++++++++++ | 67% ~10s Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 258 anchors
|+++++++++++++++++++++++++++++++++++++ | 73% ~08s Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 357 anchors
|++++++++++++++++++++++++++++++++++++++++ | 80% ~06s Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 346 anchors
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~04s Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 379 anchors
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~02s Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 353 anchors
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=31s Merging dataset 3 into 5
Extracting anchors for merged samples
Finding integration vectors
Finding integration vector weights
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Integrating data
Merging dataset 6 into 4
Extracting anchors for merged samples
Finding integration vectors
Finding integration vector weights
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Integrating data
Merging dataset 1 into 2
Extracting anchors for merged samples
Finding integration vectors
Finding integration vector weights
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Integrating data
Merging dataset 4 6 into 2 1
Extracting anchors for merged samples
Finding integration vectors
Finding integration vector weights
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Integrating data
Merging dataset 5 3 into 2 1 4 6
Extracting anchors for merged samples
Finding integration vectors
Finding integration vector weights
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Integrating data# make UMAP from integrated
seu <- int.PDHC
seu <- ScaleData(seu)Centering and scaling data matrix
|
| | 0%
|
|================================== | 50%
|
|=====================================================================| 100%seu <- RunPCA(seu, npcs = 20, verbose = FALSE)
seu <- RunUMAP(seu, reduction = "pca", dims = 1:20, n.neighbors = 81)22:20:20 UMAP embedding parameters a = 0.9922 b = 1.112
22:20:20 Read 8068 rows and found 20 numeric columns
22:20:20 Using Annoy for neighbor search, n_neighbors = 81
22:20:20 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:20:21 Writing NN index file to temp file /var/folders/k4/khtkczkd5tn732ftjpwgtr240000gn/T//Rtmp2Z2lqE/filede533410f880
22:20:21 Searching Annoy index using 1 thread, search_k = 8100
22:20:27 Annoy recall = 100%
22:20:27 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 81
22:20:29 Initializing from normalized Laplacian + noise (using irlba)
22:20:29 Commencing optimization for 500 epochs, with 831426 positive edges
Using method 'umap'
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:20:57 Optimization finishedseu <- FindVariableFeatures(seu)Warning in FindVariableFeatures.Assay(object = assay.data, selection.method = selection.method, :
selection.method set to 'vst' but count slot is empty; will use data slot instead
Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Warning in eval(predvars, data, env) : NaNs produced
Warning in hvf.info$variance.expected[not.const] <- 10^fit$fitted :
number of items to replace is not a multiple of replacement length
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|seu <- FindNeighbors(seu, dims = 1:20, k.param = 81)Computing nearest neighbor graph
Computing SNNseu <- FindClusters(seu, resolution = 0.3)Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 8068
Number of edges: 998111
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.9125
Number of communities: 12
Elapsed time: 3 secondsDimPlot(seu)
DimPlot(seu, group.by = "Line")
DimPlot(seu, group.by = "DiseaseStatus")
Annotate
gene lists
da_neurons <- c("TH","SLC6A3","SLC18A2","SOX6","NDNF","SNCG","ALDH1A1","CALB1","TACR2","SLC17A6","SLC32A1","OTX2","GRP","LPL","CCK","VIP")
NPC_orStemLike <- c("DCX","NEUROD1","TBR1","PCNA","MKI67","SOX2","NES","PAX6")
mature_neurons = c("RBFOX3","SYP","VAMP1","VAMP2","TUBB3","SYT1","BSN","HOMER1","SLC17A6")
excitatory_neurons = c("GRIA2","GRIA1","GRIA4","GRIN1","GRIN2B","GRIN2A","GRIN3A","GRIN3","GRIP1","CAMK2A")
inhbitory_neurons = c("GAD1","GAD2", "GAT1","PVALB","GABR2","GABR1","GBRR1","GABRB2","GABRB1","GABRB3","GABRA6","GABRA1","GABRA4","TRAK2")
astrocytes <- c("GFAP","S100B","AQP4","APOE", "SOX9","SLC1A3")
oligodendrocytes <- c("MBP","MOG","OLIG1","OLIG2","SOX10")
radial_glia <- c("PTPRC","AIF1","ADGRE1", "VIM", "TNC","PTPRZ1","FAM107A","HOPX","LIFR",
"ITGB5","IL6ST","SLC1A3")
epithelial <- c("HES1","HES5","SOX2","SOX10","NES","CDH1","NOTCH1")
microglia <- c("IBA1","P2RY12","P2RY13","TREM119", "GPR34","SIGLECH","TREM2",
"CX3CR1","FCRLS","OLFML3","HEXB","TGFBR1", "SALL1","MERTK",
"PROS1")
features_list <- c("MKI67","SOX2","POU5F1","DLX2","PAX6","SOX9","HES1","NES","RBFOX3","MAP2","NCAM1","CD24","GRIA2","GRIN2B","GABBR1","GAD1","GAD2","GABRA1","GABRB2","TH","ALDH1A1","LMX1B","NR4A2","CORIN","CALB1","KCNJ6","CXCR4","ITGA6","SLC1A3","CD44","AQP4","S100B", "PDGFRA","OLIG2","MBP","CLDN11","VIM","VCAM1")
short_list <- c("MKI67","SOX9","HES1","NES","DLX2","RBFOX3","MAP2","TH","CALB1","KCNJ6","SLC1A3","CD44","AQP4","S100B","OLIG2","MBP","VIM")
gene_lists = list("DA_neurons" = da_neurons, "NPC" = NPC_orStemLike,
"Neurons" = mature_neurons,
"Oligo" = oligodendrocytes, "RadialGlia" = radial_glia,
"Epithelial" = epithelial)Idents(seu) <- "integrated_snn_res.0.3"
for (i in da_neurons) {
print(FeaturePlot(seu, features = i, min.cutoff = 'q1', max.cutoff = 'q97', label = TRUE))
}Warning: Could not find TH in the default search locations, found in RNA assay instead
Warning: Could not find SLC6A3 in the default search locations, found in RNA assay instead
Warning: Could not find SLC18A2 in the default search locations, found in RNA assay instead
Warning: Could not find SNCG in the default search locations, found in RNA assay instead
Warning: Could not find ALDH1A1 in the default search locations, found in RNA assay instead
Warning: Could not find TACR2 in the default search locations, found in RNA assay instead
Warning: Could not find SLC32A1 in the default search locations, found in RNA assay instead
Warning: Could not find GRP in the default search locations, found in RNA assay instead
Warning: Could not find CCK in the default search locations, found in RNA assay instead
Warning: Could not find VIP in the default search locations, found in RNA assay instead
Warning in FeaturePlot(seu, features = i, min.cutoff = "q1", max.cutoff = "q97", :
All cells have the same value (0.648363577932972) of rna_VIP.
Make a dotplot of DA genes that are expressed
da_neurons <- c("TH","SLC18A2","SOX6","NDNF","ALDH1A1","SLC17A6","SLC32A1","OTX2","LPL")
DotPlot(seu, features = da_neurons) + RotatedAxis()Warning: Could not find TH in the default search locations, found in RNA assay instead
Warning: Could not find SLC18A2 in the default search locations, found in RNA assay instead
Warning: Could not find ALDH1A1 in the default search locations, found in RNA assay instead
Warning: Could not find SLC32A1 in the default search locations, found in RNA assay instead
DefaultAssay(seu) <- "RNA"
seu <- ScaleData(seu)Centering and scaling data matrix
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|=====================================================================| 100%Idents(seu) <- "integrated_snn_res.0.3"
for (i in NPC_orStemLike) {
print(FeaturePlot(seu, features = i, min.cutoff = 'q1', max.cutoff = 'q97', label = TRUE))
}
Idents(seu) <- "integrated_snn_res.0.3"
for (i in mature_neurons) {
print(FeaturePlot(seu, features = i, min.cutoff = 'q1', max.cutoff = 'q97', label = TRUE))
}
NA
NAIdents(seu) <- "integrated_snn_res.0.3"
for (i in excitatory_neurons) {
print(FeaturePlot(seu, features = i, min.cutoff = 'q1', max.cutoff = 'q97', label = TRUE))
}Warning in FetchData.Seurat(object = object, vars = c(dims, "ident", features), :
The following requested variables were not found: GRIN3
Error: None of the requested features were found: GRIN3 in slot data
Idents(seu) <- "integrated_snn_res.0.3"
inhbitory_neurons = c("GAD1","GAD2","PVALB","GABRB2","GABRB1","GABRB3","GABRA6","GABRA1","GABRA4","TRAK2")
for (i in inhbitory_neurons) {
print(FeaturePlot(seu, features = i, min.cutoff = 'q1', max.cutoff = 'q97', label = TRUE))
}
Idents(seu) <- "integrated_snn_res.0.3"
for (i in epithelial) {
print(FeaturePlot(seu, features = i, min.cutoff = 'q1', max.cutoff = 'q97', label = TRUE))
}
Idents(seu) <- "integrated_snn_res.0.3"
microglia <- c("P2RY12","P2RY13", "GPR34","TREM2",
"CX3CR1","OLFML3","HEXB","TGFBR1", "SALL1","MERTK",
"PROS1")
# no IBA1, TREM119"SIGLECH","FCRLS",
for (i in microglia) {
print(FeaturePlot(seu, features = i, min.cutoff = 'q1', max.cutoff = 'q97', label = TRUE))
}Warning in FeaturePlot(seu, features = i, min.cutoff = "q1", max.cutoff = "q97", :
All cells have the same value (0) of TREM2.
for (i in astrocytes) {
print(FeaturePlot(seu, features = i, min.cutoff = 'q1', max.cutoff = 'q97', label = TRUE))
}
for (i in oligodendrocytes) {
print(FeaturePlot(seu, features = i, min.cutoff = 'q1', max.cutoff = 'q97', label = TRUE))
}Warning in FeaturePlot(seu, features = i, min.cutoff = "q1", max.cutoff = "q97", :
All cells have the same value (0) of OLIG1.
for (i in radial_glia) {
print(FeaturePlot(seu, features = i, min.cutoff = 'q1', max.cutoff = 'q97', label = TRUE))
}
Get markers and check EnrichR
ClusterMarkers <- FindAllMarkers(seu, only.pos = TRUE)Calculating cluster 0
| | 0 % ~calculating
|+ | 1 % ~26s
|++ | 2 % ~24s
|++ | 3 % ~24s
|+++ | 4 % ~24s
|+++ | 5 % ~23s
|++++ | 6 % ~23s
|++++ | 7 % ~22s
|+++++ | 8 % ~23s
|+++++ | 9 % ~23s
|++++++ | 11% ~22s
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|++++++++ | 15% ~21s
|++++++++ | 16% ~21s
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|+++++++++ | 18% ~21s
|++++++++++ | 19% ~21s
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|++++++++++++++ | 26% ~19s
|++++++++++++++ | 27% ~18s
|+++++++++++++++ | 28% ~18s
|+++++++++++++++ | 29% ~18s
|++++++++++++++++ | 31% ~18s
|++++++++++++++++ | 32% ~17s
|+++++++++++++++++ | 33% ~17s
|+++++++++++++++++ | 34% ~17s
|++++++++++++++++++ | 35% ~16s
|++++++++++++++++++ | 36% ~16s
|+++++++++++++++++++ | 37% ~16s
|+++++++++++++++++++ | 38% ~16s
|++++++++++++++++++++ | 39% ~16s
|++++++++++++++++++++ | 40% ~15s
|+++++++++++++++++++++ | 41% ~15s
|++++++++++++++++++++++ | 42% ~15s
|++++++++++++++++++++++ | 43% ~14s
|+++++++++++++++++++++++ | 44% ~14s
|+++++++++++++++++++++++ | 45% ~14s
|++++++++++++++++++++++++ | 46% ~14s
|++++++++++++++++++++++++ | 47% ~13s
|+++++++++++++++++++++++++ | 48% ~13s
|+++++++++++++++++++++++++ | 49% ~13s
|++++++++++++++++++++++++++ | 51% ~13s
|++++++++++++++++++++++++++ | 52% ~12s
|+++++++++++++++++++++++++++ | 53% ~12s
|+++++++++++++++++++++++++++ | 54% ~12s
|++++++++++++++++++++++++++++ | 55% ~11s
|++++++++++++++++++++++++++++ | 56% ~11s
|+++++++++++++++++++++++++++++ | 57% ~11s
|+++++++++++++++++++++++++++++ | 58% ~11s
|++++++++++++++++++++++++++++++ | 59% ~10s
|++++++++++++++++++++++++++++++ | 60% ~10s
|+++++++++++++++++++++++++++++++ | 61% ~10s
|++++++++++++++++++++++++++++++++ | 62% ~10s
|++++++++++++++++++++++++++++++++ | 63% ~09s
|+++++++++++++++++++++++++++++++++ | 64% ~09s
|+++++++++++++++++++++++++++++++++ | 65% ~09s
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|++++++++++++++++++++++++++++++++++ | 67% ~08s
|+++++++++++++++++++++++++++++++++++ | 68% ~08s
|+++++++++++++++++++++++++++++++++++ | 69% ~08s
|++++++++++++++++++++++++++++++++++++ | 71% ~07s
|++++++++++++++++++++++++++++++++++++ | 72% ~07s
|+++++++++++++++++++++++++++++++++++++ | 73% ~07s
|+++++++++++++++++++++++++++++++++++++ | 74% ~07s
|++++++++++++++++++++++++++++++++++++++ | 75% ~06s
|++++++++++++++++++++++++++++++++++++++ | 76% ~06s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~06s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~06s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~05s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~05s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~05s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~04s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~04s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~04s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~04s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~03s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~03s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=26s Calculating cluster 1
| | 0 % ~calculating
|+ | 1 % ~20s
|++ | 2 % ~16s
|++ | 3 % ~15s
|+++ | 4 % ~15s
|+++ | 6 % ~15s
|++++ | 7 % ~15s
|++++ | 8 % ~15s
|+++++ | 9 % ~15s
|++++++ | 10% ~14s
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|+++++++ | 13% ~13s
|++++++++ | 15% ~13s
|++++++++ | 16% ~13s
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|++++++++++ | 19% ~12s
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|+++++++++++++ | 26% ~11s
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|+++++++++++++++ | 29% ~11s
|++++++++++++++++ | 30% ~11s
|++++++++++++++++ | 31% ~10s
|+++++++++++++++++ | 33% ~10s
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|+++++++++++++++++++ | 37% ~10s
|++++++++++++++++++++ | 38% ~09s
|++++++++++++++++++++ | 39% ~09s
|+++++++++++++++++++++ | 40% ~09s
|+++++++++++++++++++++ | 42% ~09s
|++++++++++++++++++++++ | 43% ~09s
|++++++++++++++++++++++ | 44% ~09s
|+++++++++++++++++++++++ | 45% ~08s
|++++++++++++++++++++++++ | 46% ~08s
|++++++++++++++++++++++++ | 47% ~08s
|+++++++++++++++++++++++++ | 48% ~08s
|+++++++++++++++++++++++++ | 49% ~08s
|++++++++++++++++++++++++++ | 51% ~07s
|++++++++++++++++++++++++++ | 52% ~07s
|+++++++++++++++++++++++++++ | 53% ~07s
|+++++++++++++++++++++++++++ | 54% ~07s
|++++++++++++++++++++++++++++ | 55% ~07s
|+++++++++++++++++++++++++++++ | 56% ~07s
|+++++++++++++++++++++++++++++ | 57% ~06s
|++++++++++++++++++++++++++++++ | 58% ~06s
|++++++++++++++++++++++++++++++ | 60% ~06s
|+++++++++++++++++++++++++++++++ | 61% ~06s
|+++++++++++++++++++++++++++++++ | 62% ~06s
|++++++++++++++++++++++++++++++++ | 63% ~06s
|+++++++++++++++++++++++++++++++++ | 64% ~05s
|+++++++++++++++++++++++++++++++++ | 65% ~05s
|++++++++++++++++++++++++++++++++++ | 66% ~05s
|++++++++++++++++++++++++++++++++++ | 67% ~05s
|+++++++++++++++++++++++++++++++++++ | 69% ~05s
|+++++++++++++++++++++++++++++++++++ | 70% ~05s
|++++++++++++++++++++++++++++++++++++ | 71% ~04s
|++++++++++++++++++++++++++++++++++++ | 72% ~04s
|+++++++++++++++++++++++++++++++++++++ | 73% ~04s
|++++++++++++++++++++++++++++++++++++++ | 74% ~04s
|++++++++++++++++++++++++++++++++++++++ | 75% ~04s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~04s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~03s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~03s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~03s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~03s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~03s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~03s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~02s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~02s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~02s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=15s Calculating cluster 2
| | 0 % ~calculating
|+ | 1 % ~01m 02s
|++ | 2 % ~59s
|++ | 3 % ~59s
|+++ | 4 % ~58s
|+++ | 5 % ~57s
|++++ | 6 % ~56s
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|+++++ | 9 % ~55s
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|++++++++++++++ | 27% ~44s
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|+++++++++++++++ | 30% ~42s
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|++++++++++++++++ | 32% ~40s
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|++++++++++++++++++ | 35% ~38s
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|+++++++++++++++++++++ | 40% ~35s
|+++++++++++++++++++++ | 41% ~34s
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|++++++++++++++++++++++ | 44% ~33s
|+++++++++++++++++++++++ | 45% ~32s
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|++++++++++++++++++++++++ | 47% ~31s
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|+++++++++++++++++++++++++ | 49% ~30s
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|++++++++++++++++++++++++++++ | 55% ~26s
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|++++++++++++++++++++++++++++++++ | 64% ~21s
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|+++++++++++++++++++++++++++++++++++++ | 73% ~15s
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|++++++++++++++++++++++++++++++++++++++ | 76% ~14s
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|+++++++++++++++++++++++++++++++++++++++ | 78% ~13s
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|+++++++++++++++++++++++++++++++++++++++++ | 81% ~11s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~10s
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|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~07s
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|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~04s
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|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=57s Calculating cluster 3
| | 0 % ~calculating
|+ | 1 % ~26s
|++ | 2 % ~25s
|++ | 3 % ~25s
|+++ | 4 % ~24s
|+++ | 5 % ~23s
|++++ | 6 % ~23s
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|+++++ | 8 % ~22s
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|++++++ | 11% ~21s
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|++++++++ | 15% ~21s
|++++++++ | 16% ~20s
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|+++++++++++++ | 24% ~18s
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|++++++++++++++ | 27% ~18s
|+++++++++++++++ | 28% ~17s
|+++++++++++++++ | 29% ~17s
|++++++++++++++++ | 31% ~17s
|++++++++++++++++ | 32% ~17s
|+++++++++++++++++ | 33% ~16s
|+++++++++++++++++ | 34% ~16s
|++++++++++++++++++ | 35% ~16s
|++++++++++++++++++ | 36% ~16s
|+++++++++++++++++++ | 37% ~15s
|+++++++++++++++++++ | 38% ~15s
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|++++++++++++++++++++ | 40% ~15s
|+++++++++++++++++++++ | 41% ~14s
|++++++++++++++++++++++ | 42% ~14s
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|+++++++++++++++++++++++ | 44% ~14s
|+++++++++++++++++++++++ | 45% ~13s
|++++++++++++++++++++++++ | 46% ~13s
|++++++++++++++++++++++++ | 47% ~13s
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|+++++++++++++++++++++++++ | 49% ~12s
|++++++++++++++++++++++++++ | 51% ~12s
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|+++++++++++++++++++++++++++ | 54% ~11s
|++++++++++++++++++++++++++++ | 55% ~11s
|++++++++++++++++++++++++++++ | 56% ~11s
|+++++++++++++++++++++++++++++ | 57% ~10s
|+++++++++++++++++++++++++++++ | 58% ~10s
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|+++++++++++++++++++++++++++++++++++ | 69% ~07s
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|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=24s Calculating cluster 4
| | 0 % ~calculating
|+ | 1 % ~55s
|++ | 2 % ~57s
|++ | 3 % ~57s
|+++ | 4 % ~57s
|+++ | 5 % ~57s
|++++ | 6 % ~01m 02s
|++++ | 7 % ~01m 01s
|+++++ | 8 % ~59s
|+++++ | 9 % ~58s
|++++++ | 10% ~57s
|++++++ | 11% ~56s
|+++++++ | 12% ~54s
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|++++++++ | 15% ~53s
|++++++++ | 16% ~51s
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|+++++++++++ | 22% ~47s
|++++++++++++ | 23% ~46s
|++++++++++++ | 24% ~45s
|+++++++++++++ | 25% ~45s
|++++++++++++++ | 26% ~44s
|++++++++++++++ | 27% ~43s
|+++++++++++++++ | 28% ~43s
|+++++++++++++++ | 29% ~42s
|++++++++++++++++ | 30% ~41s
|++++++++++++++++ | 31% ~41s
|+++++++++++++++++ | 32% ~40s
|+++++++++++++++++ | 33% ~39s
|++++++++++++++++++ | 34% ~39s
|++++++++++++++++++ | 35% ~38s
|+++++++++++++++++++ | 36% ~37s
|+++++++++++++++++++ | 38% ~37s
|++++++++++++++++++++ | 39% ~36s
|++++++++++++++++++++ | 40% ~35s
|+++++++++++++++++++++ | 41% ~35s
|+++++++++++++++++++++ | 42% ~34s
|++++++++++++++++++++++ | 43% ~33s
|++++++++++++++++++++++ | 44% ~33s
|+++++++++++++++++++++++ | 45% ~32s
|+++++++++++++++++++++++ | 46% ~32s
|++++++++++++++++++++++++ | 47% ~31s
|++++++++++++++++++++++++ | 48% ~30s
|+++++++++++++++++++++++++ | 49% ~30s
|+++++++++++++++++++++++++ | 50% ~29s
|++++++++++++++++++++++++++ | 51% ~28s
|+++++++++++++++++++++++++++ | 52% ~28s
|+++++++++++++++++++++++++++ | 53% ~27s
|++++++++++++++++++++++++++++ | 54% ~27s
|++++++++++++++++++++++++++++ | 55% ~26s
|+++++++++++++++++++++++++++++ | 56% ~25s
|+++++++++++++++++++++++++++++ | 57% ~25s
|++++++++++++++++++++++++++++++ | 58% ~24s
|++++++++++++++++++++++++++++++ | 59% ~24s
|+++++++++++++++++++++++++++++++ | 60% ~23s
|+++++++++++++++++++++++++++++++ | 61% ~22s
|++++++++++++++++++++++++++++++++ | 62% ~22s
|++++++++++++++++++++++++++++++++ | 64% ~21s
|+++++++++++++++++++++++++++++++++ | 65% ~21s
|+++++++++++++++++++++++++++++++++ | 66% ~20s
|++++++++++++++++++++++++++++++++++ | 67% ~19s
|++++++++++++++++++++++++++++++++++ | 68% ~19s
|+++++++++++++++++++++++++++++++++++ | 69% ~18s
|+++++++++++++++++++++++++++++++++++ | 70% ~17s
|++++++++++++++++++++++++++++++++++++ | 71% ~17s
|++++++++++++++++++++++++++++++++++++ | 72% ~16s
|+++++++++++++++++++++++++++++++++++++ | 73% ~16s
|+++++++++++++++++++++++++++++++++++++ | 74% ~15s
|++++++++++++++++++++++++++++++++++++++ | 75% ~14s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~14s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~13s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~13s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~12s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~11s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~11s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~10s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~10s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~09s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~08s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~08s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~07s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~07s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~06s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~05s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~05s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~04s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~04s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~03s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=57s Calculating cluster 5
| | 0 % ~calculating
|+ | 1 % ~21s
|++ | 2 % ~22s
|++ | 3 % ~21s
|+++ | 4 % ~21s
|+++ | 5 % ~21s
|++++ | 7 % ~20s
|++++ | 8 % ~20s
|+++++ | 9 % ~20s
|+++++ | 10% ~19s
|++++++ | 11% ~19s
|++++++ | 12% ~19s
|+++++++ | 13% ~19s
|++++++++ | 14% ~19s
|++++++++ | 15% ~19s
|+++++++++ | 16% ~19s
|+++++++++ | 17% ~18s
|++++++++++ | 18% ~18s
|++++++++++ | 20% ~18s
|+++++++++++ | 21% ~18s
|+++++++++++ | 22% ~17s
|++++++++++++ | 23% ~17s
|++++++++++++ | 24% ~17s
|+++++++++++++ | 25% ~17s
|++++++++++++++ | 26% ~16s
|++++++++++++++ | 27% ~16s
|+++++++++++++++ | 28% ~16s
|+++++++++++++++ | 29% ~16s
|++++++++++++++++ | 30% ~16s
|++++++++++++++++ | 32% ~15s
|+++++++++++++++++ | 33% ~15s
|+++++++++++++++++ | 34% ~15s
|++++++++++++++++++ | 35% ~15s
|++++++++++++++++++ | 36% ~14s
|+++++++++++++++++++ | 37% ~14s
|++++++++++++++++++++ | 38% ~14s
|++++++++++++++++++++ | 39% ~14s
|+++++++++++++++++++++ | 40% ~13s
|+++++++++++++++++++++ | 41% ~13s
|++++++++++++++++++++++ | 42% ~13s
|++++++++++++++++++++++ | 43% ~13s
|+++++++++++++++++++++++ | 45% ~13s
|+++++++++++++++++++++++ | 46% ~12s
|++++++++++++++++++++++++ | 47% ~12s
|++++++++++++++++++++++++ | 48% ~12s
|+++++++++++++++++++++++++ | 49% ~11s
|+++++++++++++++++++++++++ | 50% ~11s
|++++++++++++++++++++++++++ | 51% ~11s
|+++++++++++++++++++++++++++ | 52% ~11s
|+++++++++++++++++++++++++++ | 53% ~11s
|++++++++++++++++++++++++++++ | 54% ~10s
|++++++++++++++++++++++++++++ | 55% ~10s
|+++++++++++++++++++++++++++++ | 57% ~10s
|+++++++++++++++++++++++++++++ | 58% ~10s
|++++++++++++++++++++++++++++++ | 59% ~09s
|++++++++++++++++++++++++++++++ | 60% ~09s
|+++++++++++++++++++++++++++++++ | 61% ~09s
|+++++++++++++++++++++++++++++++ | 62% ~09s
|++++++++++++++++++++++++++++++++ | 63% ~08s
|+++++++++++++++++++++++++++++++++ | 64% ~08s
|+++++++++++++++++++++++++++++++++ | 65% ~08s
|++++++++++++++++++++++++++++++++++ | 66% ~08s
|++++++++++++++++++++++++++++++++++ | 67% ~07s
|+++++++++++++++++++++++++++++++++++ | 68% ~07s
|+++++++++++++++++++++++++++++++++++ | 70% ~07s
|++++++++++++++++++++++++++++++++++++ | 71% ~07s
|++++++++++++++++++++++++++++++++++++ | 72% ~06s
|+++++++++++++++++++++++++++++++++++++ | 73% ~06s
|+++++++++++++++++++++++++++++++++++++ | 74% ~06s
|++++++++++++++++++++++++++++++++++++++ | 75% ~06s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~05s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~05s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~05s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~05s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~04s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~04s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~04s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~04s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~03s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~03s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++ | 90% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=23s Calculating cluster 6
| | 0 % ~calculating
|+ | 1 % ~33s
|++ | 2 % ~31s
|++ | 3 % ~31s
|+++ | 4 % ~31s
|+++ | 5 % ~31s
|++++ | 6 % ~30s
|++++ | 7 % ~30s
|+++++ | 8 % ~29s
|+++++ | 9 % ~29s
|++++++ | 10% ~29s
|++++++ | 11% ~28s
|+++++++ | 12% ~28s
|+++++++ | 13% ~27s
|++++++++ | 14% ~27s
|++++++++ | 15% ~27s
|+++++++++ | 16% ~26s
|+++++++++ | 18% ~26s
|++++++++++ | 19% ~25s
|++++++++++ | 20% ~25s
|+++++++++++ | 21% ~25s
|+++++++++++ | 22% ~24s
|++++++++++++ | 23% ~24s
|++++++++++++ | 24% ~24s
|+++++++++++++ | 25% ~23s
|+++++++++++++ | 26% ~23s
|++++++++++++++ | 27% ~23s
|++++++++++++++ | 28% ~22s
|+++++++++++++++ | 29% ~22s
|+++++++++++++++ | 30% ~22s
|++++++++++++++++ | 31% ~21s
|++++++++++++++++ | 32% ~21s
|+++++++++++++++++ | 33% ~21s
|++++++++++++++++++ | 34% ~20s
|++++++++++++++++++ | 35% ~20s
|+++++++++++++++++++ | 36% ~20s
|+++++++++++++++++++ | 37% ~19s
|++++++++++++++++++++ | 38% ~19s
|++++++++++++++++++++ | 39% ~19s
|+++++++++++++++++++++ | 40% ~18s
|+++++++++++++++++++++ | 41% ~18s
|++++++++++++++++++++++ | 42% ~18s
|++++++++++++++++++++++ | 43% ~17s
|+++++++++++++++++++++++ | 44% ~17s
|+++++++++++++++++++++++ | 45% ~17s
|++++++++++++++++++++++++ | 46% ~16s
|++++++++++++++++++++++++ | 47% ~16s
|+++++++++++++++++++++++++ | 48% ~16s
|+++++++++++++++++++++++++ | 49% ~15s
|++++++++++++++++++++++++++ | 51% ~15s
|++++++++++++++++++++++++++ | 52% ~15s
|+++++++++++++++++++++++++++ | 53% ~14s
|+++++++++++++++++++++++++++ | 54% ~14s
|++++++++++++++++++++++++++++ | 55% ~14s
|++++++++++++++++++++++++++++ | 56% ~14s
|+++++++++++++++++++++++++++++ | 57% ~13s
|+++++++++++++++++++++++++++++ | 58% ~13s
|++++++++++++++++++++++++++++++ | 59% ~13s
|++++++++++++++++++++++++++++++ | 60% ~12s
|+++++++++++++++++++++++++++++++ | 61% ~12s
|+++++++++++++++++++++++++++++++ | 62% ~12s
|++++++++++++++++++++++++++++++++ | 63% ~11s
|++++++++++++++++++++++++++++++++ | 64% ~11s
|+++++++++++++++++++++++++++++++++ | 65% ~11s
|+++++++++++++++++++++++++++++++++ | 66% ~10s
|++++++++++++++++++++++++++++++++++ | 67% ~10s
|+++++++++++++++++++++++++++++++++++ | 68% ~10s
|+++++++++++++++++++++++++++++++++++ | 69% ~09s
|++++++++++++++++++++++++++++++++++++ | 70% ~09s
|++++++++++++++++++++++++++++++++++++ | 71% ~09s
|+++++++++++++++++++++++++++++++++++++ | 72% ~09s
|+++++++++++++++++++++++++++++++++++++ | 73% ~08s
|++++++++++++++++++++++++++++++++++++++ | 74% ~08s
|++++++++++++++++++++++++++++++++++++++ | 75% ~08s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~07s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~07s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~07s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~06s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~06s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~06s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~05s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~05s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~05s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~04s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~04s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~04s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~03s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~03s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=30s Calculating cluster 7
| | 0 % ~calculating
|+ | 1 % ~01m 35s
|++ | 2 % ~01m 34s
|++ | 3 % ~01m 35s
|+++ | 4 % ~01m 35s
|+++ | 5 % ~01m 35s
|++++ | 6 % ~01m 35s
|++++ | 7 % ~01m 34s
|+++++ | 8 % ~01m 33s
|+++++ | 9 % ~01m 31s
|++++++ | 10% ~01m 30s
|++++++ | 11% ~01m 29s
|+++++++ | 12% ~01m 28s
|+++++++ | 13% ~01m 27s
|++++++++ | 14% ~01m 26s
|++++++++ | 15% ~01m 25s
|+++++++++ | 16% ~01m 24s
|+++++++++ | 17% ~01m 23s
|++++++++++ | 18% ~01m 22s
|++++++++++ | 19% ~01m 21s
|+++++++++++ | 20% ~01m 20s
|+++++++++++ | 21% ~01m 18s
|++++++++++++ | 22% ~01m 18s
|++++++++++++ | 23% ~01m 17s
|+++++++++++++ | 24% ~01m 16s
|+++++++++++++ | 25% ~01m 15s
|++++++++++++++ | 26% ~01m 14s
|++++++++++++++ | 27% ~01m 13s
|+++++++++++++++ | 28% ~01m 12s
|+++++++++++++++ | 29% ~01m 11s
|++++++++++++++++ | 30% ~01m 10s
|++++++++++++++++ | 31% ~01m 09s
|+++++++++++++++++ | 32% ~01m 07s
|+++++++++++++++++ | 33% ~01m 06s
|++++++++++++++++++ | 34% ~01m 06s
|++++++++++++++++++ | 35% ~01m 04s
|+++++++++++++++++++ | 36% ~01m 04s
|+++++++++++++++++++ | 37% ~01m 03s
|++++++++++++++++++++ | 38% ~01m 01s
|++++++++++++++++++++ | 39% ~01m 00s
|+++++++++++++++++++++ | 40% ~59s
|+++++++++++++++++++++ | 41% ~58s
|++++++++++++++++++++++ | 42% ~57s
|++++++++++++++++++++++ | 43% ~56s
|+++++++++++++++++++++++ | 44% ~55s
|+++++++++++++++++++++++ | 45% ~54s
|++++++++++++++++++++++++ | 46% ~53s
|++++++++++++++++++++++++ | 47% ~52s
|+++++++++++++++++++++++++ | 48% ~51s
|+++++++++++++++++++++++++ | 49% ~50s
|++++++++++++++++++++++++++ | 51% ~49s
|++++++++++++++++++++++++++ | 52% ~48s
|+++++++++++++++++++++++++++ | 53% ~47s
|+++++++++++++++++++++++++++ | 54% ~46s
|++++++++++++++++++++++++++++ | 55% ~45s
|++++++++++++++++++++++++++++ | 56% ~44s
|+++++++++++++++++++++++++++++ | 57% ~43s
|+++++++++++++++++++++++++++++ | 58% ~42s
|++++++++++++++++++++++++++++++ | 59% ~41s
|++++++++++++++++++++++++++++++ | 60% ~40s
|+++++++++++++++++++++++++++++++ | 61% ~39s
|+++++++++++++++++++++++++++++++ | 62% ~38s
|++++++++++++++++++++++++++++++++ | 63% ~37s
|++++++++++++++++++++++++++++++++ | 64% ~36s
|+++++++++++++++++++++++++++++++++ | 65% ~35s
|+++++++++++++++++++++++++++++++++ | 66% ~34s
|++++++++++++++++++++++++++++++++++ | 67% ~33s
|++++++++++++++++++++++++++++++++++ | 68% ~32s
|+++++++++++++++++++++++++++++++++++ | 69% ~31s
|+++++++++++++++++++++++++++++++++++ | 70% ~30s
|++++++++++++++++++++++++++++++++++++ | 71% ~29s
|++++++++++++++++++++++++++++++++++++ | 72% ~28s
|+++++++++++++++++++++++++++++++++++++ | 73% ~27s
|+++++++++++++++++++++++++++++++++++++ | 74% ~26s
|++++++++++++++++++++++++++++++++++++++ | 75% ~25s
|++++++++++++++++++++++++++++++++++++++ | 76% ~24s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~23s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~22s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~21s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~20s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~19s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~18s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~17s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~16s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~15s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~14s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~13s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~12s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~11s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~10s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~09s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~08s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~07s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~06s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~05s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~04s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=01m 38sCalculating cluster 8
| | 0 % ~calculating
|+ | 1 % ~21s
|+ | 2 % ~22s
|++ | 3 % ~21s
|++ | 4 % ~22s
|+++ | 5 % ~21s
|+++ | 6 % ~21s
|++++ | 7 % ~21s
|++++ | 8 % ~21s
|+++++ | 9 % ~21s
|+++++ | 10% ~21s
|++++++ | 11% ~20s
|++++++ | 12% ~20s
|+++++++ | 13% ~20s
|+++++++ | 14% ~20s
|++++++++ | 15% ~20s
|++++++++ | 16% ~19s
|+++++++++ | 17% ~19s
|+++++++++ | 18% ~19s
|++++++++++ | 19% ~19s
|++++++++++ | 20% ~18s
|+++++++++++ | 21% ~18s
|+++++++++++ | 22% ~18s
|++++++++++++ | 23% ~18s
|++++++++++++ | 24% ~17s
|+++++++++++++ | 25% ~17s
|+++++++++++++ | 26% ~17s
|++++++++++++++ | 27% ~17s
|++++++++++++++ | 28% ~16s
|+++++++++++++++ | 29% ~16s
|+++++++++++++++ | 30% ~16s
|++++++++++++++++ | 31% ~16s
|++++++++++++++++ | 32% ~16s
|+++++++++++++++++ | 33% ~15s
|+++++++++++++++++ | 34% ~15s
|++++++++++++++++++ | 35% ~15s
|++++++++++++++++++ | 36% ~15s
|+++++++++++++++++++ | 37% ~15s
|+++++++++++++++++++ | 38% ~14s
|++++++++++++++++++++ | 39% ~14s
|++++++++++++++++++++ | 40% ~14s
|+++++++++++++++++++++ | 41% ~14s
|+++++++++++++++++++++ | 42% ~13s
|++++++++++++++++++++++ | 43% ~13s
|++++++++++++++++++++++ | 44% ~13s
|+++++++++++++++++++++++ | 45% ~13s
|+++++++++++++++++++++++ | 46% ~12s
|++++++++++++++++++++++++ | 47% ~12s
|++++++++++++++++++++++++ | 48% ~12s
|+++++++++++++++++++++++++ | 49% ~12s
|+++++++++++++++++++++++++ | 50% ~12s
|++++++++++++++++++++++++++ | 51% ~11s
|++++++++++++++++++++++++++ | 52% ~11s
|+++++++++++++++++++++++++++ | 53% ~11s
|+++++++++++++++++++++++++++ | 54% ~11s
|++++++++++++++++++++++++++++ | 55% ~10s
|++++++++++++++++++++++++++++ | 56% ~10s
|+++++++++++++++++++++++++++++ | 57% ~10s
|+++++++++++++++++++++++++++++ | 58% ~10s
|++++++++++++++++++++++++++++++ | 59% ~09s
|++++++++++++++++++++++++++++++ | 60% ~09s
|+++++++++++++++++++++++++++++++ | 61% ~09s
|+++++++++++++++++++++++++++++++ | 62% ~09s
|++++++++++++++++++++++++++++++++ | 63% ~09s
|++++++++++++++++++++++++++++++++ | 64% ~08s
|+++++++++++++++++++++++++++++++++ | 65% ~08s
|+++++++++++++++++++++++++++++++++ | 66% ~08s
|++++++++++++++++++++++++++++++++++ | 67% ~08s
|++++++++++++++++++++++++++++++++++ | 68% ~07s
|+++++++++++++++++++++++++++++++++++ | 69% ~07s
|+++++++++++++++++++++++++++++++++++ | 70% ~07s
|++++++++++++++++++++++++++++++++++++ | 71% ~07s
|++++++++++++++++++++++++++++++++++++ | 72% ~06s
|+++++++++++++++++++++++++++++++++++++ | 73% ~06s
|+++++++++++++++++++++++++++++++++++++ | 74% ~06s
|++++++++++++++++++++++++++++++++++++++ | 75% ~06s
|++++++++++++++++++++++++++++++++++++++ | 76% ~06s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~05s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~05s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~05s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~05s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~04s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~04s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~04s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~04s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~03s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~03s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~03s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=23s Calculating cluster 9
| | 0 % ~calculating
|+ | 1 % ~20s
|++ | 2 % ~20s
|++ | 3 % ~20s
|+++ | 5 % ~29s
|+++ | 6 % ~27s
|++++ | 7 % ~25s
|+++++ | 8 % ~24s
|+++++ | 9 % ~24s
|++++++ | 10% ~23s
|++++++ | 11% ~22s
|+++++++ | 13% ~22s
|+++++++ | 14% ~21s
|++++++++ | 15% ~20s
|+++++++++ | 16% ~20s
|+++++++++ | 17% ~19s
|++++++++++ | 18% ~19s
|++++++++++ | 20% ~19s
|+++++++++++ | 21% ~18s
|+++++++++++ | 22% ~18s
|++++++++++++ | 23% ~18s
|+++++++++++++ | 24% ~17s
|+++++++++++++ | 25% ~17s
|++++++++++++++ | 26% ~17s
|++++++++++++++ | 28% ~16s
|+++++++++++++++ | 29% ~16s
|+++++++++++++++ | 30% ~16s
|++++++++++++++++ | 31% ~15s
|+++++++++++++++++ | 32% ~15s
|+++++++++++++++++ | 33% ~15s
|++++++++++++++++++ | 34% ~15s
|++++++++++++++++++ | 36% ~14s
|+++++++++++++++++++ | 37% ~14s
|+++++++++++++++++++ | 38% ~14s
|++++++++++++++++++++ | 39% ~13s
|+++++++++++++++++++++ | 40% ~13s
|+++++++++++++++++++++ | 41% ~13s
|++++++++++++++++++++++ | 43% ~13s
|++++++++++++++++++++++ | 44% ~12s
|+++++++++++++++++++++++ | 45% ~12s
|+++++++++++++++++++++++ | 46% ~12s
|++++++++++++++++++++++++ | 47% ~12s
|+++++++++++++++++++++++++ | 48% ~11s
|+++++++++++++++++++++++++ | 49% ~11s
|++++++++++++++++++++++++++ | 51% ~11s
|++++++++++++++++++++++++++ | 52% ~10s
|+++++++++++++++++++++++++++ | 53% ~10s
|++++++++++++++++++++++++++++ | 54% ~10s
|++++++++++++++++++++++++++++ | 55% ~10s
|+++++++++++++++++++++++++++++ | 56% ~09s
|+++++++++++++++++++++++++++++ | 57% ~09s
|++++++++++++++++++++++++++++++ | 59% ~09s
|++++++++++++++++++++++++++++++ | 60% ~09s
|+++++++++++++++++++++++++++++++ | 61% ~08s
|++++++++++++++++++++++++++++++++ | 62% ~08s
|++++++++++++++++++++++++++++++++ | 63% ~08s
|+++++++++++++++++++++++++++++++++ | 64% ~08s
|+++++++++++++++++++++++++++++++++ | 66% ~07s
|++++++++++++++++++++++++++++++++++ | 67% ~07s
|++++++++++++++++++++++++++++++++++ | 68% ~07s
|+++++++++++++++++++++++++++++++++++ | 69% ~07s
|++++++++++++++++++++++++++++++++++++ | 70% ~06s
|++++++++++++++++++++++++++++++++++++ | 71% ~06s
|+++++++++++++++++++++++++++++++++++++ | 72% ~06s
|+++++++++++++++++++++++++++++++++++++ | 74% ~06s
|++++++++++++++++++++++++++++++++++++++ | 75% ~05s
|++++++++++++++++++++++++++++++++++++++ | 76% ~05s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~05s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~05s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~04s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~04s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~04s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~04s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~03s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~03s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~03s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=21s Calculating cluster 10
| | 0 % ~calculating
|+ | 1 % ~01m 00s
|++ | 2 % ~59s
|++ | 3 % ~01m 01s
|+++ | 4 % ~60s
|+++ | 5 % ~01m 01s
|++++ | 6 % ~01m 01s
|++++ | 7 % ~01m 01s
|+++++ | 8 % ~01m 00s
|+++++ | 9 % ~59s
|++++++ | 11% ~58s
|++++++ | 12% ~57s
|+++++++ | 13% ~57s
|+++++++ | 14% ~56s
|++++++++ | 15% ~55s
|++++++++ | 16% ~55s
|+++++++++ | 17% ~54s
|+++++++++ | 18% ~53s
|++++++++++ | 19% ~53s
|++++++++++ | 20% ~52s
|+++++++++++ | 21% ~51s
|++++++++++++ | 22% ~50s
|++++++++++++ | 23% ~50s
|+++++++++++++ | 24% ~49s
|+++++++++++++ | 25% ~48s
|++++++++++++++ | 26% ~47s
|++++++++++++++ | 27% ~48s
|+++++++++++++++ | 28% ~47s
|+++++++++++++++ | 29% ~46s
|++++++++++++++++ | 31% ~46s
|++++++++++++++++ | 32% ~45s
|+++++++++++++++++ | 33% ~44s
|+++++++++++++++++ | 34% ~44s
|++++++++++++++++++ | 35% ~43s
|++++++++++++++++++ | 36% ~42s
|+++++++++++++++++++ | 37% ~41s
|+++++++++++++++++++ | 38% ~41s
|++++++++++++++++++++ | 39% ~40s
|++++++++++++++++++++ | 40% ~39s
|+++++++++++++++++++++ | 41% ~38s
|++++++++++++++++++++++ | 42% ~38s
|++++++++++++++++++++++ | 43% ~37s
|+++++++++++++++++++++++ | 44% ~36s
|+++++++++++++++++++++++ | 45% ~36s
|++++++++++++++++++++++++ | 46% ~35s
|++++++++++++++++++++++++ | 47% ~34s
|+++++++++++++++++++++++++ | 48% ~33s
|+++++++++++++++++++++++++ | 49% ~33s
|++++++++++++++++++++++++++ | 51% ~32s
|++++++++++++++++++++++++++ | 52% ~31s
|+++++++++++++++++++++++++++ | 53% ~31s
|+++++++++++++++++++++++++++ | 54% ~30s
|++++++++++++++++++++++++++++ | 55% ~29s
|++++++++++++++++++++++++++++ | 56% ~29s
|+++++++++++++++++++++++++++++ | 57% ~28s
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|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=01m 05sCalculating cluster 11
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|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=36s library(enrichR)Welcome to enrichR
Checking connection ...
Enrichr ... Connection is Live!
FlyEnrichr ... Connection is available!
WormEnrichr ... Connection is available!
YeastEnrichr ... Connection is available!
FishEnrichr ... Connection is available!
OxEnrichr ... Connection is available!setEnrichrSite("Enrichr") # Human genesConnection changed to https://maayanlab.cloud/Enrichr/
Connection is Live!# list of all the databases
# get the possible libraries
dbs <- listEnrichrDbs()
# this will list the possible libraries
dbs
# select libraries with cell types
db <- c('CellMarker_Augmented_2021','Azimuth_Cell_Types_2021')
# function for a quick look
checkCelltypes <- function(cluster_num = 0){
clusterX <- ClusterMarkers %>% filter(cluster == cluster_num & avg_log2FC > 0.25)
genes <- clusterX$gene
# the cell type libraries
# get the results for each library
clusterX.cell <- enrichr(genes, databases = db)
# visualize the results
print(plotEnrich(clusterX.cell[[1]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value", title = 'CellMarker_Augmented_2021'))
print(plotEnrich(clusterX.cell[[2]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value", title = 'Azimuth_Cell_Types_2021'))
}
Top markers
top5
top2 <- ClusterMarkers %>% group_by(cluster) %>% top_n(n=2, wt =avg_log2FC)
DoHeatmap(seu, features = top5$gene, size = 3, angle = 90, group.by = "integrated_snn_res.0.3")
DoHeatmap(seu, features = top2$gene, size = 3, angle = 90, group.by = "integrated_snn_res.0.3")
quick check EnrichR
checkCelltypes(cluster_num = 11)Uploading data to Enrichr... Done.
Querying CellMarker_Augmented_2021... Done.
Querying Azimuth_Cell_Types_2021... Done.
Parsing results... Done.
Add annotations
DimPlot(seu, label = FALSE, group.by = "DiseaseStatus")
Save the annotated neurons
saveRDS(seu, "/Users/rhalenathomas/Documents/Data/scRNAseq/ParseExample/Ex2_48well/Ex2NeuronsIntPDandHC.RDS")
---
title: "R Notebook"
output: html_notebook
---

```{r}
library(Seurat)
library(tidyverse)
#library(CelltypeR)
```



```{r}
seu <- readRDS("/Users/rhalenathomas/Documents/Data/scRNAseq/ParseExample/Ex2_48well/Parse48_analysisMaria.RDS")

unique(seu$sample)

Idents(seu) <- "sample"
levels(seu)

samples <- levels(seu)
samples

```

Add meta data information

```{r}
# using celltypeR function

# add the culture type NPC, DA neurons 2weeks, Motor neurons
new_vector <- ifelse(grepl("NPC", samples), "NPC",
                     ifelse(grepl("Final", samples), "Neurons2weeks",
                            ifelse(grepl("^M", samples), "MotorNeurons", "Other")))

# Print the new vector
print(new_vector)

seu <- annotate(seu, annotations = new_vector, to_label = "sample",annotation_name = "CultureType")

DimPlot(seu)

# check with sample names

table(seu$sample, seu$CultureType)


```

Add the lines and then the diseases status

```{r}

  # Create a new vector with line names
new_line_vector <- gsub("^(NPC|Final)", "", samples)
new_line_vector <- gsub("3123$", "3123-iso", new_line_vector)
new_line_vector[new_line_vector == "P65P"] <- "3123-isoP65P"
new_line_vector[new_line_vector == "Q65Q"] <- "3123-isoQ65Q"

# Print the new vector with line names


new_line_vector[new_line_vector == "2AIW002-02"] <- "AIW002-02"

print(new_line_vector)

seu <- annotate(seu, annotations = new_line_vector, to_label = "sample",annotation_name = "Line")


# check
table(seu$sample, seu$Line)

```
```{r, fig.width=6}
DimPlot(seu)
```


```{r}
# add disease status

# Create the lookup table for Line to DiseaseStatus mapping
line_to_disease <- c("3448" = "HC",
                     "TD22" = "HC",
                     "AIW001-02" = "HC",
                     "AIW002-02" = "HC",
                     "2965" = "PD",
                     "3383" = "PD",
                     "3575" = "PD",
                     "TD06" = "PD",
                     "TD07" = "PD",
                     "3123" = "PD",
                     "3567" = "PD",
                     "3595" = "PD",
                     "3940" = "PD",
                     "3567-iso" = "PD-iso",
                     "3940R-iso" = "PD-iso",
                     "TD06-iso" = "PD-iso",
                     "3940N-iso" = "PD-iso",
                     "3123-isoQ65Q" = "PD-iso",
                     "3123-isoP65P" = "PD-iso",
                     "3123-iso" = "PD-iso",
                     "3383-iso" = "PD-iso",
                     "3575-iso" = "PD-iso",
                     "3595-iso" = "PD-iso")

# Assuming "Line" is your starting character vector
Idents(seu) <- "Line"
Line <- levels(seu)
# Create a new vector of disease statuses using the lookup table
disease_status_vector <- ifelse(Line %in% names(line_to_disease), 
                                line_to_disease[Line], 
                                "other")

# Print the new vector of disease statuses
print(disease_status_vector)

seu <- annotate(seu, annotations = disease_status_vector, to_label = "Line",annotation_name = "DiseaseStatus")


# check
table(seu$Line, seu$DiseaseStatus)


```

```{r}
cellcounts <- as.data.frame(table(seu$Line,seu$CultureType))
table(seu$Line,seu$CultureType,seu$DiseaseStatus)

# Convert long_df to the wide format
wide_df <- pivot_wider(cellcounts, 
                       id_cols = Var1, 
                       names_from = Var2, 
                       values_from = Freq)

write.csv(wide_df,"cellcounts_sample.csv")

```

```{r}
DimPlot(seu, group.by = "DiseaseStatus")

```




Subset for to remove pools and Maria's samples

```{r}
unique(seu$sample)
Idents(seu) <- "DiseaseStatus"

seu.n <- subset(seu, idents = c("HC","PD","PD-iso"))
dim(seu.n)
dim(seu)

DimPlot(seu.n)

```
Reprocess subset

```{r}

seu.n <- RunPCA(seu.n, npcs = 20, verbose = FALSE)
seu.n <- RunUMAP(seu.n, reduction = "pca", dims = 1:20, n.neighbors = 81)
DimPlot(seu.n)
seu.n$CultureType
DimPlot(seu.n, group.by = "CultureType")
DimPlot(seu.n, group.by = "Line")

```

```{r}
seu.n <- readRDS("/Users/rhalenathomas/Documents/Data/scRNAseq/ParseExample/Ex2_48well/ParseNCADDsamples.RDS")
  
```



Get clusters

```{r}
seu.n <- NormalizeData(seu.n)
seu.n <- FindVariableFeatures(seu.n)
seu.n <- FindNeighbors(seu.n, dims = 1:20, k.param = 81)
seu.n <- FindClusters(seu.n, resolution = 0.3)
DimPlot(seu.n, group.by = "RNA_snn_res.0.3")
#seu.n$RNA_snn_res.0.3
DimPlot(seu.n, group.by = "CultureType")

```





Subset out neurons and NPC

```{r}

#seu.n$CultureType
Idents(seu.n) <- "CultureType"
neurons <- subset(seu.n, idents = "Neurons2weeks")

Idents(seu.n) <- "CultureType"
NPC <- subset(seu.n, idents = "NPC")

```

```{r}

seu <- neurons
seu <- RunPCA(seu, npcs = 20, verbose = FALSE)
seu <- RunUMAP(seu, reduction = "pca", dims = 1:20, n.neighbors = 81)
seu <- FindVariableFeatures(seu)
seu <- FindNeighbors(seu, dims = 1:20, k.param = 81)
seu <- FindClusters(seu, resolution = 0.3)
DimPlot(seu)
DimPlot(seu, group.by = "CultureType")
DimPlot(seu, group.by = "Line")

neurons <- seu

```



```{r}
seu <- NPC
seu <- RunPCA(seu, npcs = 20, verbose = FALSE)
seu <- RunUMAP(seu, reduction = "pca", dims = 1:20, n.neighbors = 81)
seu <- FindVariableFeatures(seu)
seu <- FindNeighbors(seu, dims = 1:20, k.param = 81)
seu <- FindClusters(seu, resolution = 0.3)
DimPlot(seu)
DimPlot(seu, group.by = "CultureType")
DimPlot(seu, group.by = "Line")

NPC <- seu

DimPlot(NPC, group.by = "DiseaseStatus")

```



save objects 
```{r}

saveRDS(seu, "/Users/rhalenathomas/Documents/Data/scRNAseq/ParseExample/Ex2_48well/Parse48_allSamples.RDS")

saveRDS(seu.n, "/Users/rhalenathomas/Documents/Data/scRNAseq/ParseExample/Ex2_48well/ParseNCADDsamples.RDS")
saveRDS(neurons, "/Users/rhalenathomas/Documents/Data/scRNAseq/ParseExample/Ex2_48well/ParseNCADDsamplesNeurons.RDS")
saveRDS(NPC, "/Users/rhalenathomas/Documents/Data/scRNAseq/ParseExample/Ex2_48well/ParseNCADDsamplesNPC.RDS")


neurons <- readRDS("/Users/rhalenathomas/Documents/Data/scRNAseq/ParseExample/Ex2_48well/ParseNCADDsamplesNeurons.RDS")


```

```{r}
table(neurons$Line)
Idents(neurons) <- "Line"
levels(neurons)
table(neurons$DiseaseStatus,neurons$Line)

```

Now integrate samples

```{r}

# All samples is too large to integrate 

Idents(neurons) <- "DiseaseStatus"
levels(neurons)
neur.PD <- subset(neurons, idents = "PD")
neur.PDiso <- subset(neurons, idents = "PD-iso")
neur.HC <- subset(neurons, idents = "HC")

# make a list of seurat objects by our cell type variable
# will integrate the PD, PD-iso, HC separately and then merge them will see if will integrate or not

```

```{r}
# an integrate function

# Define a function for integrating a list of Seurat objects
integrate_seurat_objects <- function(seurat_list, dims = 1:30) {
  # Normalize and find variable features for each object
  for (i in 1:length(seurat_list)) {
    seurat_list[[i]] <- NormalizeData(seurat_list[[i]], verbose = FALSE)
    seurat_list[[i]] <- FindVariableFeatures(seurat_list[[i]], selection.method = "vst")
  }

  # Create an empty Seurat object to store the integrated data
  integrated_seurat <- subset(seurat_list[[1]])

  # Iterate over the list of Seurat objects
  for (i in 1:length(seurat_list)) {
    # Rename the 'orig.ident' metadata inside the Seurat object to match the object name in the list
    seurat_list[[i]]$orig.ident <- names(seurat_list)[i]
  }

  sample.list <- seurat_list
  for (i in 1:length(sample.list)) {
    # Normalize and scale the data
    sample.list[[i]] <- NormalizeData(sample.list[[i]], verbose = FALSE)
    sample.list[[i]] <- ScaleData(sample.list[[i]], verbose = FALSE)
    # Find variable features
    sample.list[[i]] <- FindVariableFeatures(sample.list[[i]], selection.method = "vst")
    # Get the variable features
    variable_features <- VariableFeatures(sample.list[[i]])
    # Run PCA with the variable features
    sample.list[[i]] <- RunPCA(sample.list[[i]], verbose = FALSE, npcs = 30, features = variable_features)
  }

  # Find integration anchors
  int.anchors <- FindIntegrationAnchors(object.list = sample.list, dims = dims, reduction = "rpca")

  # Integrate data
  integrated_seurat <- IntegrateData(anchorset = int.anchors, dims = dims)

  return(integrated_seurat)
}



```

Use function to integrate the HC

```{r}

sublist <- SplitObject(neur.HC, split.by = "Line")

int.HC <- integrate_seurat_objects(sublist, dims = 1:30)
rm(sublist)
sublist <- SplitObject(neur.PD, split.by = "Line")

int.PD <- integrate_seurat_objects(sublist, dims = 1:30)
rm(sublist)
sublist <- SplitObject(neur.PDiso, split.by = "Line")

int.PDiso <- integrate_seurat_objects(sublist, dims = 1:30)


```



```{r}

# Create a list of the three Seurat objects
DefaultAssay(int.HC) <- "RNA"
DefaultAssay(int.PD) <- "RNA"
#DefaultAssay(int.PDiso) <- "RNA" # didn't save
Neuron_list <- list("HC" = int.HC,"PD" = int.PD)


# Integrate the Seurat objects
integrated_seurat <- integrate_seurat_objects(Neuron_list, dims = 1:20)



```

```{r}
# save integrated objects
saveRDS(int.HC, "/Users/rhalenathomas/Documents/Data/scRNAseq/ParseExample/Ex2_48well/ParseNCADDsamplesNeuronsintHC.RDS")
saveRDS(int.PD, "/Users/rhalenathomas/Documents/Data/scRNAseq/ParseExample/Ex2_48well/ParseNCADDsamplesNeuronsintPD.RDS")
saveRDS(int.PDiso, "/Users/rhalenathomas/Documents/Data/scRNAseq/ParseExample/Ex2_48well/ParseNCADDsamplesNeuronsintPDiso.RDS")

```

Read in the integrated objects and then integrate all 3

```{r}

int.HC <- readRDS("/Users/rhalenathomas/Documents/Data/scRNAseq/ParseExample/Ex2_48well/ParseNCADDsamplesNeuronsintHC.RDS")
int.PD <- readRDS("/Users/rhalenathomas/Documents/Data/scRNAseq/ParseExample/Ex2_48well/ParseNCADDsamplesNeuronsintPD.RDS")
int.PDiso("/Users/rhalenathomas/Documents/Data/scRNAseq/ParseExample/Ex2_48well/ParseNCADDsamplesNeuronsintPD.RDS")

```

Integrated only the PD and HC


```{r}
# make UMAP from integrated
seu <- integrated_seurat
seu <- ScaleData(seu)
seu <- RunPCA(seu, npcs = 20, verbose = FALSE)
seu <- RunUMAP(seu, reduction = "pca", dims = 1:20, n.neighbors = 81)
seu <- FindVariableFeatures(seu)
seu <- FindNeighbors(seu, dims = 1:20, k.param = 81)
seu <- FindClusters(seu, resolution = 0.3)
DimPlot(seu)
DimPlot(seu, group.by = "Line")
DimPlot(seu, group.by = "DiseaseStatus")


```


Samples are not well integrated.  I'll try to integrate by lines

```{r}

# subset just disease and control
Idents(neurons) <- "DiseaseStatus"
Neu.HCPD <- subset(neurons, idents = c("HC","PD"))
sublist <- SplitObject(Neu.HCPD, split.by = "Line")



# Integrate the Seurat objects
int.PDHC <- integrate_seurat_objects(sublist, dims = 1:20)



```

```{r}
# make UMAP from integrated
seu <- int.PDHC
seu <- ScaleData(seu)
seu <- RunPCA(seu, npcs = 20, verbose = FALSE)
seu <- RunUMAP(seu, reduction = "pca", dims = 1:20, n.neighbors = 81)
seu <- FindVariableFeatures(seu)
seu <- FindNeighbors(seu, dims = 1:20, k.param = 81)
seu <- FindClusters(seu, resolution = 0.3)
DimPlot(seu)
DimPlot(seu, group.by = "Line")
DimPlot(seu, group.by = "DiseaseStatus")

```


Annotate

gene lists 
```{r}

da_neurons <- c("TH","SLC6A3","SLC18A2","SOX6","NDNF","SNCG","ALDH1A1","CALB1","TACR2","SLC17A6","SLC32A1","OTX2","GRP","LPL","CCK","VIP")
NPC_orStemLike <- c("DCX","NEUROD1","TBR1","PCNA","MKI67","SOX2","NES","PAX6")
mature_neurons = c("RBFOX3","SYP","VAMP1","VAMP2","TUBB3","SYT1","BSN","HOMER1","SLC17A6")
excitatory_neurons = c("GRIA2","GRIA1","GRIA4","GRIN1","GRIN2B","GRIN2A","GRIN3A","GRIN3","GRIP1","CAMK2A")
inhbitory_neurons = c("GAD1","GAD2", "GAT1","PVALB","GABR2","GABR1","GBRR1","GABRB2","GABRB1","GABRB3","GABRA6","GABRA1","GABRA4","TRAK2")
astrocytes <- c("GFAP","S100B","AQP4","APOE", "SOX9","SLC1A3")
oligodendrocytes <- c("MBP","MOG","OLIG1","OLIG2","SOX10")

radial_glia <- c("PTPRC","AIF1","ADGRE1", "VIM", "TNC","PTPRZ1","FAM107A","HOPX","LIFR",
              "ITGB5","IL6ST","SLC1A3")
epithelial <- c("HES1","HES5","SOX2","SOX10","NES","CDH1","NOTCH1")

microglia <- c("IBA1","P2RY12","P2RY13","TREM119", "GPR34","SIGLECH","TREM2",
               "CX3CR1","FCRLS","OLFML3","HEXB","TGFBR1", "SALL1","MERTK",
               "PROS1")

features_list <- c("MKI67","SOX2","POU5F1","DLX2","PAX6","SOX9","HES1","NES","RBFOX3","MAP2","NCAM1","CD24","GRIA2","GRIN2B","GABBR1","GAD1","GAD2","GABRA1","GABRB2","TH","ALDH1A1","LMX1B","NR4A2","CORIN","CALB1","KCNJ6","CXCR4","ITGA6","SLC1A3","CD44","AQP4","S100B", "PDGFRA","OLIG2","MBP","CLDN11","VIM","VCAM1")

short_list <- c("MKI67","SOX9","HES1","NES","DLX2","RBFOX3","MAP2","TH","CALB1","KCNJ6","SLC1A3","CD44","AQP4","S100B","OLIG2","MBP","VIM")


gene_lists = list("DA_neurons" = da_neurons, "NPC" = NPC_orStemLike,
                  "Neurons" = mature_neurons, 
                  "Oligo" = oligodendrocytes, "RadialGlia" = radial_glia,
                  "Epithelial" = epithelial)
```

```{r}
Idents(seu) <- "integrated_snn_res.0.3"

for (i in da_neurons) {
  print(FeaturePlot(seu, features = i, min.cutoff = 'q1', max.cutoff = 'q97', label = TRUE))
}
```

Make a dotplot of DA genes that are expressed

```{r}
da_neurons <- c("TH","SLC18A2","SOX6","NDNF","ALDH1A1","SLC17A6","SLC32A1","OTX2","LPL")

DotPlot(seu, features = da_neurons) + RotatedAxis()


```

```{r}
DefaultAssay(seu) <- "RNA"
seu <- ScaleData(seu)
Idents(seu) <- "integrated_snn_res.0.3"

for (i in NPC_orStemLike) {
  print(FeaturePlot(seu, features = i, min.cutoff = 'q1', max.cutoff = 'q97', label = TRUE))
}
```
```{r}
Idents(seu) <- "integrated_snn_res.0.3"

for (i in mature_neurons) {
  print(FeaturePlot(seu, features = i, min.cutoff = 'q1', max.cutoff = 'q97', label = TRUE))
}


```

```{r}
Idents(seu) <- "integrated_snn_res.0.3"

for (i in excitatory_neurons) {
  print(FeaturePlot(seu, features = i, min.cutoff = 'q1', max.cutoff = 'q97', label = TRUE))
}


```

```{r}

Idents(seu) <- "integrated_snn_res.0.3"
inhbitory_neurons = c("GAD1","GAD2","PVALB","GABRB2","GABRB1","GABRB3","GABRA6","GABRA1","GABRA4","TRAK2")

for (i in inhbitory_neurons) {
  print(FeaturePlot(seu, features = i, min.cutoff = 'q1', max.cutoff = 'q97', label = TRUE))
}

```

```{r}
Idents(seu) <- "integrated_snn_res.0.3"

for (i in epithelial) {
  print(FeaturePlot(seu, features = i, min.cutoff = 'q1', max.cutoff = 'q97', label = TRUE))
}

```
```{r}
Idents(seu) <- "integrated_snn_res.0.3"
microglia <- c("P2RY12","P2RY13", "GPR34","TREM2",
               "CX3CR1","OLFML3","HEXB","TGFBR1", "SALL1","MERTK",
               "PROS1")
# no IBA1, TREM119"SIGLECH","FCRLS",
for (i in microglia) {
  print(FeaturePlot(seu, features = i, min.cutoff = 'q1', max.cutoff = 'q97', label = TRUE))
}

```
```{r}
for (i in astrocytes) {
  print(FeaturePlot(seu, features = i, min.cutoff = 'q1', max.cutoff = 'q97', label = TRUE))
}
```

```{r}
for (i in oligodendrocytes) {
  print(FeaturePlot(seu, features = i, min.cutoff = 'q1', max.cutoff = 'q97', label = TRUE))
}
```
```{r}
for (i in radial_glia) {
  print(FeaturePlot(seu, features = i, min.cutoff = 'q1', max.cutoff = 'q97', label = TRUE))
}
```

Get markers and check EnrichR

```{r}

ClusterMarkers <- FindAllMarkers(seu, only.pos = TRUE)

```

```{r}
library(enrichR)
setEnrichrSite("Enrichr") # Human genes
# list of all the databases
# get the possible libraries
dbs <- listEnrichrDbs()

# this will list the possible libraries
dbs

# select libraries with cell types
db <- c('CellMarker_Augmented_2021','Azimuth_Cell_Types_2021')

# function for a quick look
checkCelltypes <- function(cluster_num = 0){
  clusterX <- ClusterMarkers %>% filter(cluster == cluster_num & avg_log2FC > 0.25)
  genes <- clusterX$gene
  # the cell type libraries
  # get the results for each library
  clusterX.cell <- enrichr(genes, databases = db)
  # visualize the results
print(plotEnrich(clusterX.cell[[1]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value", title = 'CellMarker_Augmented_2021'))
print(plotEnrich(clusterX.cell[[2]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value", title = 'Azimuth_Cell_Types_2021'))

}

```
Top markers

```{r}
head(ClusterMarkers)
top5 <- ClusterMarkers %>% group_by(cluster) %>% top_n(n=5, wt =avg_log2FC)

DefaultAssay(seu) <- "RNA"
seu <- NormalizeData(seu)


top5

top2 <- ClusterMarkers %>% group_by(cluster) %>% top_n(n=2, wt =avg_log2FC)
DoHeatmap(seu, features = top5$gene, size = 3, angle = 90, group.by = "integrated_snn_res.0.3")

DoHeatmap(seu, features = top2$gene, size = 3, angle = 90, group.by = "integrated_snn_res.0.3")
```

quick check EnrichR

```{r}

checkCelltypes(cluster_num = 11)

```
Add annotations

```{r}

library(CelltypeR)
celltypes1 <- c("Neuron1","Neuron2","Epithelial","Neuron3", "Astro","Neuron4-TH","Neuron5",
                "Stem","Neuron6","Neuron7", "Pericyte", "Neuron8")

seu <- annotate(seu, annotations = celltypes1, to_label = "integrated_snn_res.0.3",annotation_name = "Celltypes1")
DimPlot(seu, label = TRUE, group.by = "Celltypes1")

celltypes2 <- c("imNeuron-DA","imNeuron-GABA-DA","Epithelial","imNeuron-DA",
                "Astro","imNeuron-Glut-DA","imNeuron-DA",
                "Stem","imNeuron-Glut","imNeuron-DA", "Pericyte", "NPC")

seu <- annotate(seu, annotations = celltypes2, to_label = "integrated_snn_res.0.3",annotation_name = "Celltypes2")
Idents(seu) <- "Celltypes2"
DimPlot(seu, label = TRUE)

celltypes3 <- c("imNeuron-DA","imNeuron-DA","Epithelial","imNeuron-DA",
                "Astro","imNeuron-DA","imNeuron-DA",
                "Stem","imNeuron","imNeuron-DA", "Pericyte", "NPC")

seu <- annotate(seu, annotations = celltypes3, to_label = "integrated_snn_res.0.3",annotation_name = "Celltypes3")
DimPlot(seu, label = TRUE)

```

```{r}
DimPlot(seu, label = FALSE, group.by = "DiseaseStatus")
```



Save the annotated neurons

```{r}

saveRDS(seu, "/Users/rhalenathomas/Documents/Data/scRNAseq/ParseExample/Ex2_48well/Ex2NeuronsIntPDandHC.RDS")

```


